For field-like environmentalgas monitoring and noninvasive illnessdiagnostics, effective sensing materials with exceptional sensingcapabilities of sensitive, quick detection of volatile organic compounds(VOCs) are required. Carbon-based nanomaterials (CNMs), like CNTs,graphene, carbon dots (Cdots), and others, have recently drawn a lotof interest for their future application as an elevated-performancesensor for the detection of VOCs. CNMs have a greater potential fordeveloping selective sensors that target VOCs due to their tunablechemical and surface properties. Additionally, the mechanical versatilityof CNMs enables the development of novel gas sensors and places themahead of other sensing materials for wearable applications. An overviewof the latest advancements in the study of CNM-based sensors is givenin this comprehensive organized review. They are presentin a variety of products. Due to higher volatility, mobility, andresistance to degradation, VOCs can travel over large distances inthe atmosphere.2  is one of the most prominent analytical techniques for identifyingVOCs.19 Carbon-basednanomaterials (CNMs) are appealing prospects for the future of automatedsensor technology because of their excellent sensing detectabilityand intriguing transduction capabilities. A majority of atoms in 0–2-dimensionalCNMs are exposed to the outside environment due to their huge specificsurface area and remarkable sensitivity.22 Low-dimensionalCNMs like CNTs, graphene, and MXene have a large specific surfacearea to reach a high detection sensitivity for VOCs; however, themajority of their atoms are discharged into the air.24 We focused on the most recent developments in CNMs for VOC sensingin this comprehensive review.Nowadays, carbon-based nanomaterials are encouraging the scientificcommunity to make powerful sensor devices due to their characteristicstructure, especially graphene and its derivatives such as grapheneoxide (GO), CNTs, reduced graphene oxide (rGO), Cdots, and MXene.Several ways of interacting with VOCs, such as π–πstacking, electrostatic forces, and noncovalent bonding, make themsuitable candidates for adsorbents.226 The CNT, which is a circular tube-like structuredue to its rolled up graphene sheets, can be a single-walled or multi-walledCNT. Hence, it is considered a 1D carbon material; its length canbe extended to some micrometers long, whereas its diameter can befound to be in the nm range.28 Carbon dots (CDs) aregaining much more attention because of their biocompatibility, excellentphysicochemistry, and tunable photoluminescence and are being exploredas biosensors.30 They are a new type of carbonnanostructure of >10 nm size and the most water-soluble carbonmaterial.31 The effective visualizationof their shape andsize can be studied by a TEM image, which has been shown in The carbon nanomaterials can be synthesizedwith controlled shapeand size from 0D to 2D, whereas 3D carbon materials are consideredas graphite, diamond, etc. Except for graphite and diamond, we havefocused on graphene, CNTs, CDs, and other carbon-based nanomaterials.When we talk about nanomaterials, their shapes and sizes are morecritical because of surface catalytic reactions. The reduced graphene(rGO) obtained by heating or using a reducing agent on GO sheets isnot spherical. Its nominal effective diameter can be calculated insteadof the actual size using dynamic light-scattering (DLS) techniques.Through this technique, different scientists measured the averagesizes of rGO sheet combinations of honeycomb structures of a carbonatom, which were found to be 2.93 μm, 0.5–5 μm,etc.35 This degree of oxidationis responsible for the electrical conductivity of modified grapheneand the degree of oxidation depending on various synthetic routes.The carbon atoms of modified oxidized graphene are partially sp3 hybridized and can move above and below the plane.36 Another derivative of graphene-like sheets canbe obtained by reducing graphene oxide through chemical reductionor simple heating methods. Park and Ruoff examined the elemental analysisof carbon and oxygen atoms  for rGOmade by combustion techniques and discovered that rGO is not the sameas pure graphene since it contains a considerable quantity of oxygen.Furthermore, when cooled to lower temperatures, its conductivity reducesby 3 orders of magnitude, causing it to display nonmetallic behavior.37 In this way, a very clear difference can beseen between pristine graphene and graphene derivatives such as GOand rGO. Both pristine graphene and its derivatives are two-dimensionalstructures. Still, pristine graphene is relatively inefficient forthe adsorption of VOC molecules. In contrast, its derivatives havemore adsorption sites due to the presence of different oxygen groupsand hence offer high sensitivity toward VOC molecules of the film.38 On the other hand, the current–voltage(I–V) characteristics forboth GO and rGO exhibit ohmic contact, which is suitable for gas-sensingapplications.39The other derivatives or oxidizedforms of graphene by oxygen functionalgroups such as carboxyl (−C=O), epoxy (−C–O–C−),and hydroxyl (−OH) make graphene oxide suspended in water andother polar media.32-hybridized atomic orbitals, and as a result, reduced grapheneoxide (rGO) became a conductive active material for molecular sensors.The rolling form of the graphene sheet results in π confinement due to σ–π rehybridizationat its circular curvature structure and creates an asymmetric distributionof σ-bonds and π-orbitals on both sides (inside and outside)of the nanotube, forming a high π-electron conjugation outsidethe nanotube.40 Thus, the surface of CNTscan donate or withdraw electrons from any electron-donating or electron-withdrawingentity during molecular interaction, thereby modulating overall electricalresistance.42 Several groups have approachedthe molecular orbital (MO) theory for the electronic structure ofCQDs, which demonstrated that transitions have occurred in n →π* and π → π* as available transition energies.45 According to Larson and Hu, the aromatic sp2-hybridizedcarbons are responsible for their π-states. The presence ofπ-electron conjugation results in high charge transfer in carbon-basedmaterials.46 Generally, the detection ofVOCs is governed by their interaction with carbon-based materialsby diffusion and sensed by the active center of carbon-based materials,50 microgravimetric sensors,55 and resistive sensors.59 When a molecule species interacts with the sidewallsof carbon-based or any semiconductor materials during gas detection,changes in conductance61 (due to charge transfer or mobilitychange) or capacitance62 (from inherentor induced dipole moments) occur.65 The interaction betweenthe VOCs and carbon-based compounds  depends stronglyon the architecture and design of the composites or materials whichmay have a larger contact area, thus allowing greater sensitivity.The CNT has two variables, SWCNT and MWCNT. Depending upon chirality,SWCNTs can be semiconducting or metallic,67 and MWCNTs have only a metallic character and are therefore unsuitablefor fabricating gas transistors.It is very much essential to note the originof electrical conductivityproperties of graphene, CNTs, and Cdots for sensing properties. Itwill help the understanding of surface interaction as a chemisorptionreaction which changes the electrical properties of carbon-based materials.The 2D graphene sheet shows high carrier mobility due to planar sp470 Meanwhile,research into the construction of sophisticated sensors for VOC detectionis expanding rapidly .72 A thorough explanation of the VOC-detecting device’s sensingprocess is initially required to construct a powerful VOC sensor.Recently, point-of-care applications, various diseasediagnoses,atmospheric gas tracking, and other fields have significantly increasedthe demand for accurate VOC gas analysis.75 research toward sensing technologies. These materials have beenshown to be more effective in producing higher VOC sensors.78 Three steps commonly take place in gas sensors manufactured usingCNMs for VOC detection: (i) trapping of VOC gas, (ii) their interactionwith the sensing active site, and (iii) dispersing of the VOC gas.Focusing on the complex interactions between the VOC gas and the activecenter that detects them, CNMs have been used to create a range ofVOC gas sensors, including optical81 and microgravimetric ones.86 Regarding optical sensors, spectrometric or colorimetric variationsin the sensing materials are brought on by the intermolecular interactionsamong VOC samples and the active center81 and resistance-based sensors.90Research on developing gas sensors has primarily focused on nanoparticlesbecause of their superior physicochemical characteristics and highsurface-to-volume ratio. The recent discovery of CNMs has prompted591Due to the considerable electrochemical, mechanical,optical, andthermal properties of CNMs, many research studies have been done inthis area during the last three decades. Fullerenes, carbon nanotubes,graphene, carbon dots, carbon nanohorns, and carbon black are examplesof zero-, one-, two-, and three-dimensional CNMs that have shown suchinherent properties that can be easily modified in the developmentof cutting-edge innovation for sensing applications. Nanomaterialshave opened up new pathways and options for sensing analysts or targetmolecules.93Carbon-based nanostructureshave several benefits over other commonlyused sensor materials, including simple manufacturing procedures,unexpected physiochemical characteristics, greater sensing properties,environmentally friendly materials, improved detection accuracy, highflexibility, sensitivity, and reliability. They also have a largesurface-to-volume ratio, high stability, high electrical conductivity,biocompatibility, reduced size, and an improved surface area. CNMsare being studied as a result of further utilization of efficientsensor technology.94 The many CNM-based sensorsutilized for VOC detection are listed in Carbon nanostructures likeCNTs and graphene can be used to detectextremely small levels of danger and greenhouse gases. Therefore,using the CNM-based sensor to develop susceptible, small energy gassensors is both of significant academic interest and of enormous economicsignificance.62 carbon atoms organized in cylindricaltubes with diameters ranging from 1 to 100 nm. Single-wall carbonnanotubes (SWCNTs) and multiwalled carbon nanotubes (MWCNTs) are thetwo types of CNTs used most commonly. MWCNTs, on the other hand, arecomposed of multiple sheets of concentric single-walled graphene cylinderswith an interlayer spacing of 3.4 and are held together by van derWaals forces.111 SWCNTs are the rolledform of the graphene sheet. Hexagonal rings make up CNTs and controltheir size, curvature, and electronic properties. Chirality refersto the configuration of carbon hexagonal rings in nanotubes.112A one-dimensional carbon allotrope known as a carbonnanotube (CNT)is composed of sp113 CVD,114 and laser ablation115 are common techniques for making CNTs. The CVD process and the dischargemethod are more advantageous for the mass production of high-qualityCNTs.114Since 20 years, CNTs have been made ona massive scale for numeroususes. Arc discharge,116 A quick, painless, inexpensive, noninvasivemethod for early disease detection and ongoing physiological monitoringis breathing analysis.118 Analyzing the diaphragmaticmovement when breathing can help detect human disorders like atypicalsleep problems, asthma, heart attack, and lung cancer early on.120 Due to their appealing qualities, such as good biocompatibility,wearing comfort, low cost, as well as sensitivity to breathing activitiesin the aspect of lower frequencies and slight amplitude body motions,triboelectric nanogenerators (TENG) have recently been widely usedfor self-powered respiration monitoring. TENG-based respiration sensorscan accurately and continuously track physiological respiratory behaviorsand exhaled chemical regents for individualized health care.121 Further, the 'Liu' research groupdesigneda respiration-driven triboelectric sensor (RTS) for concurrent biomechanicaland biochemical breath measurement that can directly transform breathflow into electricity.122 The Wang researchgroup also created an integrated triboelectric self-powered respirationsensor (TSRS) to track both human breathing patterns and the amountof NH3 in exhaled gases.123One of the oldest clinicalpractices is breathing analysis usedfor medical purposes. The binding of volatile compounds to a sensorarray, the creation of sensor modifications, which produce distinctivepatterns of signals, and the integration of signal patterns allowingcategorization are the main phases in the breathing analysis workingprinciple, which also is based on the human olfactory system.3 in exhaled gases. 124 Su et al. created an alveolus-inspired membranesensor (AIMS) for self-powered wearable nitrogen dioxide detectionand personal physiological evaluation in 2020. This sensor may beused to detect human breath activities for breath analysis.125'Su' and his research team created a triboelectric self-poweredrespiration sensor (TSRS) in 2019 to monitor human breathing patternsand the amount of NH2S below breathing concentrationlevels in humid airflow (sc-SWCNTs). The repeatable sensor constructionmethod is based on controlled multiplexing dielectrophoretic sc-SWCNTdeposition. The sensing region is developed and produces gold nanoparticlesthat solve detection at room temperature by leveraging the affinityof gold and sulfur atoms in the gas. The estimated limit of detection(LOD) for sensing devices functionalized with optimum nanoparticledispersion is 3 ppb, and their sensitivity is 0.122%/ppb. Our sensorssurpass certain electrochemical sensors that seem to be availableon the market in terms of improved stability and response levels despiteself-validation.126Using a self-validating 64-channel sensor array based on semiconductingsingle-walled carbon nanotubes, the Panes–Ruiz research groupshowed precise detection of Hn-pentane.Except for isopropanol, the SDBS-CNT sensors could detect water, acetone,chloroform, and ethanol but not many biomarkers. N-Pentane, acetone, isoprene, and ethanol were all reactive substancesfor the BnzlkCl-CNT sensors. Although it was shown that pristine CNTsresponded best to the majority of the aromatic VOCs in the collection,CTAB-CNT sensors were just moderately sensitive to most VOCs and lackedsignificant selectivity.127According tothe kind and concentration of various surfactants,Chatterjee et al. investigated how several lung cancer VOCs foundin human breath impacted the selectivity and sensitivity using CNT-basedsensors . The performance of thesensors is found to be affected by both the interaction of the surfactantwith the analyts and the supramolecular assembly with CNTs. Surfactant–CNTsensors were produced using the spray LbL approach. Water and otherpolar VOCs, particularly methanol and other alcohols, have demonstratedgood sensitivity to CNT-DOC sensors. TX405-CNT sensors were sensitiveto three chemicals: benzene, chloroform, and 128 The total sensor system also benefits from this drop injunction potential. 129Moreover,'Sinha' research group used zinc oxide (ZnO) and CNTsto create a composite-based chemiresistive sensor. A mechanism hasbeen linked to the VOC adsorption process’s temperature- andmaterial-dependent switching. Due to their simplicity in low-temperaturesynthesis and the large variety of VOC sensing, stability, or otheradvantageous qualities, ZnO/CNT composites have been selected in thiswork as a crucial material above other metal oxides. Additionally,it is anticipated that a p–n heterojunction will develop atevery point in which CNT and ZnO are in contact. This will also impactthe composite sensor’s ability to sense. A potential is createdas an outcome of the interaction of n-type (ZnO) and p-type (CNT)materials. As a result, if VOCs were adsorbed onto the surface ofa composite sensor, their potential should decrease.For the purpose of detecting volatileVOCs, the Turner researchgroup developed vapor quantum resistive sensors (vQRSs) utilizingrandom networks made of carbon nanotubes (CNTs) that have been functionalizedwith caffeine. Energy-dispersive X-ray spectroscopy (EDS) and a localizedtransmission electron microscopy (TEM) image both demonstrate thatcaffeine has moved to the CNT–CNT junction (CNTj). Furthermore,at the CNT intersection, we conducted localized EDS 5a,b. Acc130Specific VOCs like acetone, toluene,and ethanol have increasedsensitivity and distinct selectivity and a quick response time withcaffeine-enhanced CNT junction (Caf-CNTj) vQRSs. According to moleculardynamics simulations, the sensor’s sensitivity is diffusion-limited,with ethanol actively contributing (MD). The created Caf-CNTj sensorscould be a strong contender for various activities performed by anelectronic nose, including breath analysis and food quality monitoring.131Bohli et al. created a room-temperaturetoluene and benzene sensorin 2019 using MWCNTs functionalized with a long-chain thiol self-assembledmonolayer, 1-hexadecanethiol (HDT), and decorated with Au nanoparticles.The thiol monolayer adhering to the MWCNTs was examined using FT-IRand high-resolution TEM, which were used to describe the gold nanoparticledecorating. The ability of Au-MWCNT and HDT/Au-MWCNT sensors to detectVOCs at levels as low as ppm is evidence that the self-assembled layerenhances the sensing selectivity, sensitivity by a factor of 17, andresponse dynamics. The response of the Au-MWCNT sensor to variousvapor injection amounts for toluene and benzene at room temperatureis shown in 4 microflowers (MFs) were used toembellish MWCNTsin composites for the detection of NH3. The material’sporous nature, the large specific surface area, and the p–nheterojunction formed by the MWNTs and NiWO4 contributeto the better sensing capabilities of this composite. The sensor basedon 10% (MWN10) has the best gas sensitivity, with a sensitivity of13.07 to 50 ppm of NH3 at room temperature and a detectinglower limit of 20 ppm, according to the gas sensitivity of the sensorbased on daisy-like NiWO4/MWCNTs.132NiWO7133 winningthem the Nobel Prize for their contribution to Physics in 2010.134 Pure graphene and graphene oxide (GO) are thefamily members of “graphene”. Graphene is a single layerof graphite formed of predictable hexagonal arrangements of 2D carbonatoms. This arrangement is identical to that of graphite. Grapheneis relatively light, weighing just around 0.77 mg per square meter.135Graphene has garnered a great deal of scientific attention sinceNovoselov and associates used mechanical exfoliation to synthesizeit for the first time in 2004,2 V1 s1), much more significant than carbon nanotubes (CNTs),is made possible by its zero band gap.136 It possesses exceptional mechanical strength, an extremely highsurface-to-volume ratio,138 high capacitance,140 outstanding thermal conductivity,141 exceptionalelectrical conductivity, and the possibility for atomically cleangraphene sheets on the graphene lattice.142 In addition to such amazing properties, graphene also enables sensitiveanalyte detection because of its exceptionally low electrical noise.143 Functionalized reduced graphene oxide (RGO)VOC sensors for detecting the ppm level of VOCs was developed by Tombelet al. . The three VOCs that werethe focus of the detection studies were acetone, toluene, and isoprene.These experiments were conducted at ambient temperatures of 30 °Cand 40% relative humidity.145Smart electronic material 'Graphene'has many unique propertiesand distinguishing characteristics. The structure’s extremelyhigh electron mobility  in various ratios. This nanocompositeis utilized as a sensor to detect NH3, and it performssubstantially superior to pure reduced graphene-oxide-based gas sensors,exhibiting an outstanding response (19.2%) to NH3 at ambienttemperature.147Reduced graphene oxide (rGO)/metalloporphyrin-basedVOC sensorsare simple and affordable to make and can precisely detect VOCs linkedto a number of diseases often present in human breath. They have beendescribed by the Lee research group. Using a drop casting technique,we created an rGO-metalloporphyrin-sensing array and examined it withEDS and scanning electron microscopy (SEM). Next, three disease-relatedbreath VOCs , as well as carbonmonoxide, were introduced to the detecting array.4 cm2/(V s), and low resistivity of4 × 10–1 cm, acetone molecules are more readilyabsorbed on the surface of rGO thin films. As a result of the rGOsensing material used in the QCM gas sensor, rapid reactions are possible.The QCM gas sensors’ quick reaction and recovery periods of20–30 s have been proven using commercially available rGO thinsensing films.148Gupta et al. investigatedthe use of thin coating rGO as a sensingmaterial on a QCM sensor to detect VOC vapors at ambient temperature.To make the rGO suspension, the aqueous suspension of GO paste isfirst reduced with ascorbic acid. The rGO thin films were cast usingthe drop-cast technique and dried at room temperature. For their contaminant-freewrinkled form, multilayered graphene structure, high mobility of 2× 10149It is claimed thatto produce nanostructured detectors GO nanodomainsof p-type were carefully inserted into an n-type 3D ZnO nanoarchitecture.These ultraporous nanoheterojunction networks’ features wereinvestigated using physical and chemical approaches, demonstratinghow GO affects the networks’ ability to sense chemicals andlight. By employing UV light activation of the sensing reactions,these nanocomposite materials were also employed to detect many commonVOCs, including ethyl alcohol, propanone, and ethylbenzene, down toambient temperature. Here, 150The two main forms of sensitizing agents, N-doped graphene quantumdots (N-GQDs-NSs) coupled to nanosheets and N-doped carbon nanoparticles(N-CNPs) 9,have b2 nanoparticle (NPs)/GO heterojunction areused. P-type anatase TiO2 NPs made from sol–gelwere used as the channel materials and were implanted in a few stackedGO channels. To determine the ideal ratio of GO and TiO2 NPs within the hybrid channel, extensive microscopic, spectroscopic,and electrical characterizations were carried out. On a SiO2/Si substrate, a back-gated FET sensor was created and put throughtesting while being exposed to various VOCs. At 100 °C, testswith p-TiO2/GO FET sensors at zero gate voltage (VGS =0) demonstrated ethanol selectivity. The lower detection limit ofethanol was enhanced to 500 ppb by utilizing an appropriate gate voltage, significantly enhancing its sensitivityand p-TiO2–GO hybrid for field-assisted sensitivityamplification. Transient behavior in the presence of reducing vaporethanol, on the other hand, served as evidence for the p-type conductanceof the composites of p-TiO2 NPs and p-GO. The pure GO sensor (S9)could not respond at 100 ppm, but the S1–S8 sensors produceda response within the dynamic range of 25–300 ppm. Every sensordemonstrated a stable baseline with predictable sensing behavior with a p-TiObehavior 10.1513O4/rGO composite.It features a low working temperature and a wide detection range.The hydrothermal Sn3O4/rGO composite exhibitsa large specific surface area and a microstructure like a flower.The sensing features of sensors based on pure Sn3O4 and Sn3O4/rGO composites are thoroughlyanalyzed to learn how the heterostructure influences the sensing performanceof HCHO. According to the observed data, the Sn3O4/rGO composite sensor has a significantly higher sensing responseat a lower working temperature than the pure Sn3O4 sensor. 152A susceptible HCHO sensor is suggested and demonstrated using aheterostructure of the Sn2 can cause asthma and other illnesses in people.153 Researchers are concentrating on creating light,affordable wearable electronics with excellent performance, and adaptabilitydue to the recent rapid advancement of gas sensor research.154 Chen and a colleague created a smartphone-enabled,completely integrated wireless system for real-time NO2 monitoring using a flexible ZnS nanoparticle/nitrogen-doped reducedgraphene oxide (ZnS NP/N-rGO) sensor. The device exhibited a responseof 2.2–10 ppm of NO2, a rapid recovery performanceof 724 s, a power consumption of 0.52 W, and an extremely low theoreticallimit of detection (69 ppb) for graphene-based sensors.155As one of the most frequently harmful irritatinggases, NO8156 For use in acetone gas-sensing applications,the Mishra research group reports producing ZnS quantum dots (QDs)via a hot-injection technique , an emerging nanomaterial of the carbon-based materialsfamily, have attracted significant research interest.echnique 12. The g166An acetone sensor was created using a quartz crystalmicrobalance(QCM), which was modified with graphene quantum dots (GQDs) to sensegases. GQDs were produced via citrate pyrolysis, and their characteristicswere determined by high-resolution TEM (HR-TEM). They looked at thesensor’s gas sensitivity to acetone at low concentrations.With a sensitivity of 16.78 Hz/ppm and a minimal detection limit of2.5 ppm, it demonstrated good linearity at less than 240 ppm acetoneconcentrations. The sensor showed high acetone selectivity in a combinationof acetone, butanol, and isopropanol 13. The s9167 The absorption energy of material to certaingas molecules, as well as the associated charge transfer characteristics,are important factors in predicting sensing qualities based on gassensing. Yu et al. performed the first computational analysis to demonstrateMXenes’ potential as a gas sensor or capturer, employing monolayerTi2CO2 for NH3 detection.168 Because metallic Ti2C(OH)2, Ti2CF2, and Ti2C do not have semiconductingproperties, only Ti2CO2 was studied using first-principlesmodeling. Using the most stable structure, the O-functionalized monolayerof Ti2CO2, gas molecules’ possible absorptionpositions were investigated to compute adsorption energy and chargetransfer. Aside from demonstrating gas absorption and charge transferin MXenes, plane-wave-based DFT computation allows for the tailoringof MXene selectivity via controlled oxygen functionalization. Junkaewet al. investigated gas molecule adsorption behavior on four MXenes and their O-terminated surfaceswith electronic charge characteristics.168 When a gas molecule is absorbed, it retains its molecule form oris dissociated on the surface of the MXenes. Theoretical simulationspredicted varied absorption energies of multiple gaseous species onvarious types of MXene surface conditions and their associated chargetransfer for gas-sensing properties. However, just a few laboratoryexperiments on the sensing properties of different MXenes have beenpublished. We report a novel experimental investigation into the gas-sensingproperties of MXenes. Layered transition metal carbides, nitrides,or MXene are composed of more than 60 different types, including Ti2C, V2C, Nb2C, Ti4N3, TiNbC, Ti3CN, and Mo2TiC2.171 By chemically treating precursors of the Mn+1AXn phase, also known as theMAX phase, where M is an early transition metal, A is typically anelement IIIA or IVA; X is C and/or N; and n = 1,2, and 3 MXenes are produced.172 MXenesare a focus of research because of a number of intriguing characteristicsthat promise to lead the way for future sensor development. A fascinatingclass of two-dimensional materials called MXenes has lately attractedinterest for their possible use in gas sensing.173The gas-sensing properties of MXenemay be predicted using thequantum mechanical approach and basic principles based on densityfunctional theory (DFT). Using the Schrödinger equation, DFT-basedfirst-principles examine the electronic structure of materials.2CTx MXene sensor in detecting VOCs. Ona Si/SiO2 substrate, the suggested sensor is a chemiresistivedevicemade by photolithography. The effectiveness of the sensor is examinedin relation to different MXene manufacturing conditions. DifferentVOC concentrations, including ethanol, toluene, benzene, acetone,and methanol, are examined at room temperature. The effectivenessof several MXene-based gas sensors is compared in 174Here, we show the efficiency of a new Mo3C2Tx and WSe2  upon the surfaces of 2D Ti3C2Tx Mxene sheets. The 1D W18O49 NRs were uniformly distributed throughout the surfaces of a 2D Ti3C2Tx sheet to producethe 1D/2D W18O49/Ti3C2Tx hybrids. Comparing the W18O49/Ti3C2Tx composites to the Ti3C2Tx sheets and W18O49 NRs, itwas discovered that they markedly increased the ability to sense acetone.The hybrid sensor demonstrated a robust response to low acetone concentrations,rapid response and recovery times, excellent selectivity, long-termstability, and an extremely low acetone detection limit. The superbsensing performance of the hybrid sensor can be attributed to theuniform dispersion of W18O49 NRs on the surface,the elimination of fluorine-containing groups during the solvothermalprocess, and the beneficial interactions at the interface betweenthe W18O49/Ti3C2Tx sheets and W18O49 NRs.181We providea simple solvothermal method for fabricating W3C2Tx–ZnO nanosheethybrids have been producedfor NO2 recovery and detection under UV light. By usingHF solution to etch the Ti3AlC2 Al layer, Ti3C2Tx nanosheets wereproduced (MAX). ZnO nanosheets’ porous structure is crucialfor the adsorption of NO2 gas. With the aid of UV irradiationduring the recovery process, the Ti3C2Tx–ZnO nanosheet-based gas sensor displayedbetter NO2-detecting skills, including a high sensitivityof 367.63% to 20 ppm of NO2 and a quicker response/recoverytime of 22 s/10 s.182Schottky-barrier-equipped Tix)/graphite (G)nanofilament-based buckypaper sensor was developedby Homaeigohar (183The first oxygenatedamorphous carbon (a-COaeigohar 15. By fo10The development of VOC sensing utilizing CNMsis outlined in thiscomprehensive review. Due to the wide range of uses for VOC environmentalmonitoring, the difficulty of selecting VOC detection is growing everyday. Toxic VOC detection is crucial for protecting the environmentand human health. Due to their unique morphology and features, developingCNMs has been the foundation for sensor technology over the precedingtwo decades. CNMs like graphene, CNTs, and their derivatives, whichhave a large number of adsorption sites, low density, tunable electricalproperties, high carrier mobility, low operating temperatures, longerlives, and ease of recovery, are appropriate sensing materials fora variety of toxic pollutant gases and volatile organic compounds.Numerous researchers have observed an increase in sensitivity thatallows for detecting harmful VOCs at ppb levels. The selectivity ofa CNM-based sensor toward a specific VOC at room temperature has beenshown in numerous papers. According to the literature, metal oxideand nanocarbon hybrids have outstanding sensing capabilities, withnanocarbon serving as the primary component. Composites made of nanocarbonhave shown a strong potential for use in VOC sensing.However,some measure of research should be focused on the synthesisor design of porous or hierarchical structures of carbon-based materialsfor the suitable VOC molecule adsorption with more reaction siteswhich can help with a better understanding of structural-adsorption/absorptionproperty relationships that will garnish sensing mechanisms. Also,by analysis of theoretical calculation, the change of electronic propertiesof host–guest items can be explored for selective gas sensors.


RSC Advances article. These are given below as ref. The authors are publishing this correction to draw the reader’s attention to their closely related papers that should have been cited as ref. 42 and 43 in this F1/F0 were selected to evaluate the effects of the aforementioned reaction conditions on the sensing performance of the developed strategy, where F1 and F0 were the fluorescence intensities of the solutions in the presence and absence of CRP, respectively.42”On page 11963, a citation to ref. 42 should be added to the end of the sentence beginning “The fluorescence intensity…”. Therefore, the sentence should be changed to “The fluorescence intensity and the value of −1 were performed.43”On page 11965–11966, a citation to ref. 43 should be added at the end of the sentence beginning “The detection of CRP…”. Therefore, the sentence should be changed to “The detection of CRP in biological samples by spiking in CRP to human serum, urine and saliva diluted to 10% with buffer solution with the final concentration of 10 ng mLThe authors also regret that there are portions of unattributed text overlap in the Introduction, Results and discussion and Conclusion sections with other papers, including ref. 42 and 43.The Royal Society of Chemistry apologises for these errors and any consequent inconvenience to authors and readers.


The most important types of such cross-communities are the densest holonic ones, because they exhibit many interesting properties. For example, such a cross-community can represent a portion of users, who share all the following traits: ethnicity, religion, neighbourhood, and age-range. The denser a multi-profiled cross-community is, the more granular and holonic it is and the greater the number of its members, whose interests are exhibited in the common interests of the entire cross-community. Moreover, the denser a cross-community is, the more specific and distinguishable its interests are . Unfortunately, methods that advocate the detection of granular multi-profiled cross-communities have been under-researched. Most current methods detect multi-profiled communities without consideration to their granularities. To overcome this, we introduce in this paper a novel methodology for detecting the smallest and most granular multi-profiled cross-community, to which an active user belongs. The methodology is implemented in a system called ID_CC. To improve the accuracy of detecting such cross-communities, we first uncover missing links in social networks. It is imperative for uncovering such missing links because they may contain valuable information . We evaluated ID_CC by comparing it experimentally with eight methods. The results of the experiments revealed marked improvement.Most realistic social communities are  A massive number of complex scientific problems have been depicted and represented as network structures for empirical studies. These network representations solve many different scientific fields, such as biological systems , ecosystThe methods that cluster data based on its attributes can be broadly classified into the following: (1) methods that use the structural relationships between nodes  as guidance of the clustering procedure ; (2) metMost of the above-mentioned methods can detect real-world communities according to specific properties; yet many of them can detect only heterogeneous communities and communities with certain topological structures , 11. To Ahn et al.  proposedk-cliques in a network and then identifies communities and their overlaps by carrying out clique-clique overlap distribution analysis using the following four quantities: (1) the number of communities, to which each node belongs, (2) the number of nodes shared by two communities, (3) the number of links in a community, and (4) the number of nodes in a community.Palla et al.  proposedMany methods that combine clustering analysis and attribute information have been proposed for detecting communities using node attribute information. Aggarwa et al.  proposedMost realistic social communities are multi-attributed cross-communities constructed from users sharing some commonalities. The challenge is how to detect a multi-attributed cross-community from a multi-attributed network, whose some of its attributes are unlabelled. Detecting such a cross-community requires constructing community-specific modelling techniques capable to infer the distinguishable characteristics of each cross-community. The techniques should include mechanisms able at identifying the distinguishable characteristics of each attribute. As can be seen from the current methods described previously that they are not equipped with such mechanisms, except for, to some degree, CoRel . Even CoImplicit Detector of Cross-Communities (ID_CC). ID_CC detects a cross-community at the granularity of a k-clique. Current methods that adopt the k-clique approach for detecting the cross-community to which an active user belongs (such as [k-clique procedure for extracting cross-nodes at the granularity of a cross-communities (as opposed to a cross-k-cliques). Detecting cross-k-cliques requires quantifying the extent to which each pair of k-cliques are associated. To the best of our knowledge, our method is the first that perform the following:k-cliques are associated. Current methods simply use the structural positioning of k-cliques in a network to assess their relationships. For example, Palla et al. [local influence of the pair of cliques’ interaction in associating their overlapped communities and the global influence of the pair in associating the other communities in the network. That is, to extract a cross-communities at the granularity of a k-clique, ID_CC quantifies the following:The pair of clique’s binary influence, which is the extent to which the pair’s interaction influences the relationship between their overlapped committees.The pair of cliques’ global influence, which is the extent to which the pair’s interaction influences the relationships among all communities in the network.Quantifying the extent to which a et al.  used cliCi and Cj be two interrelated cliques residing in communities CMTYx and CMTYy respectively. Let CMTYz be an arbitrary community in the network that is not overlapped with CMTYx and CMTYy. ID_CC quantifies the influence of the interaction between Ci and Cj in transmitting information between CMTYx and CMTYy  and in transmitting information between CMTYx and CMTYz and between CMTYy and CMTYz .Let Inferring missing links prior to detecting cross-communities using novel mechanisms.Employing a novel mechanism that can implicitly infer an active user’s undeclared communities that match his own social traits.Most current methods detect multi-profiled communities without consideration to their granularities. To overcome this, we introduce in this paper a novel methodology for detecting the smallest and most granular multi-profiled cross-communities. We implemented the methodology in a working system called (such as ), employSince there are always new users wishing to join existing cross-communities, we incorporated a functionality to ID_CC that detects the smallest and most granular multi-profiled cross-community, to which an active user belongs. First, the system infers the comprehensive list of the user’s communities based on a few communities, to which the user declared membership. That is, the system implicitly infers the user’s undeclared communities that match his own social traits. Then, the system infers the smallest and most granular multi-profiled cross-community, to which the user belongs by analysing the hierarchical interrelationships between the detected user’s communities. The system considers all cross-profiles that come to existence from the interrelationships between the hierarchically overlapped user’s communities. The larger the number of inferred user’s communities, the denser and more specific is the multi-profiled cross-community identified by the system for the user.multi-profiled cross-communities constructed from users sharing commonalities that include adaptive social profile ingredients. A community defined by the commonality of its adaptive social profile is a one constructed according to the natural adaptation to a certain social trait as opposed to being constructed due to involuntary circumstances . The interests of a multi-profiled cross-community are the union of the interests of the various communities, from which the cross-community is constructed.A community is a social entity with specific social rules and dynamicity commonalities. We observe that most realistic social communities are densest holonic ones, because they exhibit many interesting properties. For example, such a cross-community can represent a portion of users, who share all the following traits: ethnicity, religion, neighbourhood, and age-range. The denser a multi-profiled cross-community is, the more granular and holonic it is and the greater the number of its members, whose interests are exhibited in the common interests of the entire cross-community. The likelihood of an exact match between the interests of an active user and the interests of his cross-community increases as the cross-community becomes denser. The denser such a cross-community is, the more specific and distinguishable its interests are from other cross communities.The most important types of multi-profiled cross-communities are the The links in most social networks are not exhaustive. The sharing of some social characteristics between a pair of communities may not always be reflected by a link connecting the pair in the social network. Most often, this happens when a social network depicts a dataset containing some members, who did not fully disclose and declare their memberships to all the communities, to which they belong . It is imperative for uncovering such missing links because they may contain valuable information . Therefore, detecting cross-communities should be preceded by uncovering missing links.Methods that advocate the detection of granular multi-profiled cross-communities have been under-researched. Most these methods detect cross-communities without consideration to their granularities.Our methodology is based on the following observations, which shed the light on the importance of detecting granular multi-profiled cross-communities and missing links:Single-Attribute community” (SAC) throughout the article to refer to an aggregation of individuals who share a common single attribute . This concept is formalized in definition 1.We use the term “Definition 1—Single-Attribute Community (SAC): SAC is a group G of individuals within a social network  with schema , where each x, y ∈ G (x ≠ y) share one common single attribute mapping ψ: V → R and relation mapping ∂: E → L.Multi-Attribute Community (MAC). A MAC is formed from an aggregation of individuals who all belong to two or more SACs. That is, the common characteristic shared by these individuals are the attributes of several SACs. Thus, a MAC is an aggregation of members who share common multi-attributed traits. Intuitively, the size of such a MAC is smaller than the size of each of the SACs, to whom its individuals belong. An Overlapping Multi-Attribute Community (OMAC) is a MAC formed from an aggregation of individuals who all belong to two or more SACs of different attributes. That is, an OMAC is a body of members who share the common characteristics of some cross-community’s multiple attributes.The denser a community is, the more distinct and specific are its common concerns and interests. Therefore, we propose a granular and specific class of community called Example: Consider user member 18 in x), community 3 represents a religion R(y), and community 4 represents a national origin O(z). Then, the densest and most granular multi-profiled cross-community, to which user 18 belongs will be formed from an aggregation of individuals who belong to the same ethnic group E(x), follow the identical religion R(y), and are descendants of the matching national origin O(z). Thus, such OMAC is constructed from the following intersection: E(x) ∩R(y) ∩O(z). The interests and concerns of this OMAC are more specific and granular than the ones of each of E(x), R(y), and O(z) individually.k-clique model. A k-clique is a defined as complete graph with k nodes. A pair of adjacent k-cliques shares k-1 nodes. We now formalize these concepts.An effective mechanism for identifying the influential nodes in a SAC is to first represent the SAC using Definition 1—Clique: A clique C in a graph G is a subset of the nodes of G such that every two nodes in C are adjacent. Thus, C is a complete induced subgraph.Definition 2—k-Clique: It is a clique of a subcommunity that has k nodes. Each two adjacent k-cliques Ci and Cj in the subcommunity share k − 1 nodes. That is, Ci ∩ Cj = k − 1.Maximal k-Clique Sub-SAC (MKCSS). A MKCSS is a subcommunity within a SAC formed from the maximal union of k-cliques within the SAC, where each two k-cliques in the subcommunity is k-clique connected. It is a fully connected (complete) subcommunity of k-cliques within a SAC. We now formalize these concepts.We introduce the concept of Definition 3—Maximal k-Clique Sub-SAC (MKCSS): It is a maximal union of k-cliques within a SAC, where each two k-cliques Ci and Cj in the subcommunity is k-clique connected. Ci and Cj are k-clique connected, if there is a series of k-cliques Cx, …, Cy, such that each two adjacent cliques in the series Ci, Cx, …, Cy, Cj share k -1 nodes.Lemma 1: Any two k-cliques in a MKCSS are k-clique connected.Proof: If we consider a scenario of |MKCSS| = k + 1, any two k-cliques k + 1 nodes. This is because k + 2, any two k-cliques k + 1. Since: (a) k-clique in k-clique in k-clique connected. The above holds for any |MKCSS| > k.Running Example 1: MKCSS Relationship Graph (MRG), which depicts the relationships between the MKCSSs of a same SAC as well the interrelationships between the MKCSSs of different SACs. Each node in the MRG represents a MKCSS. Two nodes in the MRG are connected by an edge if they share at least one cross-member. Thus, two SACs in the MRG are connected by an edge if they share at least one cross-member. We now formalize this concept.We now introduce the concept of Definition 4: MKCSS Relationship Graph (MRG): An MRG is an undirected graph G , where V is the set of nodes and E is the set of edges in the graph. Each node in G represents an MKCSS in some SAC. The weight of an MKCSS node is the number of unique k-cliques in the MKCSS. Two nodes ni, nj ∈ V are connected by an edge e ∈ E, if ni and nj share at least one common individual member .Theorem 1: Since the weight of a MKCSS node is the number of its unique k-cliques, the weight of the MKCSS node equals |MKCSS|! (k! (|MKCSS| − k)!)Proof: The number of unique k-cliques in an MKCSS is the number of unique k-combination of a subset of k distinct nodes that belong to the MKCSS. The number of these k-combinations equals the binomial coefficient, which can be depicted using factorials as follows: |MKCSS|! (k! (|MKCSS| − k)!).Running Example 2: i in i in i ) in i in i and Mj be two MKCSSs that belong to two SACs and share cross-members. Mi and Mj in the MRG will be linked by an Association Edge to denote that they share cross-members. For example, there are three Association Edges connecting SACs 2 and 7 in the MRG in An Association Edge denotes cross-members shared by two interrelated MKCSSs that belong to two different SACs. Let Mlocal influence of an Association Edge.A Binary Influence (BI) is a score that quantifies the degree of influence of an Association Edge in associating the two SACs at its end points. That is, the score quantifies the extent to which the cross-members shared by two SACs relate the two SACs. Thus, BI is an indicator of the global influence in the entire MRG. Thus, GI reflects the global relative interaction role and influence of an Association Edge in passing information to the entire network.A Global Influence (GI) is a score that quantifies the degree of influence of an Association Edge in associating all SACs in MRG. That is, the score characterizes an Association Edge’s For convenient reference, we list in Computing the Binary Influence of each Association Edge: ID_CC computes the BI score of each Association Edge  connecting a pair of SCAs Sμ and Sν in MRG. The score reflects the local influence of  relative to the other Association Edges connecting Sμ and Sν.Computing the Global Influence of each Association Edge: ID_CC computes the GI score of each Association Edge  connecting a pair of SCAs Sμ and Sν in the MRG. The score reflects the average chance of  relative to the other Association Edges connecting Sμ and Sν in passing information between: (a) Sμ and Sν, and (b) the remaining SACs in the MRG. The formula for computing GI considers various factors such as the BI of  as a fraction of the BIs of all Association Edges that pass information between: (a) Sμ or Sν, and (b) the remaining SACs in the MRG.Uncovering Missing Association Edges in the MKCSS Relationship Tree: First, ID_CC converts the MRG into a tree data structure for the ease of uncovering missing Association Edges connecting hierarchical interrelated SACs. We call the resulting structure MKCSS Relation Tree. Then, ID_CC uncovers the missing Association Edges using a concept that we call Relevant Lowest Common Ancestor (RLCA), which helps in inferring related nodes based on the relationships between their ancestor nodes. Finally, ID_CC computes the BIs and GIs of the implicitly identified Association Edges.Determining the Densest Multi-Profiled Cross-SACs to which an Active User Belongs: ID_CC applies the Maximum Spanning Tree (MaxST) algorithm [implicitly identified. The resulting intersection of all SACs in the list is the densest and most granular multi-profiled cross-SACs that matches the user’s own social traits.lgorithm , 23 on tBelow are the sequential processing steps taken by our proposed system ID_CC:local influence of the Association Edge [ę denote one of these two edges. As demonstrated by ę to pass a message between the two SACs is impacted by the following factors:ę. Consider that some node n belongs to SAC 1 needs to send a message to some node ń residing in SAC 2. From the two Association Edges, intuitively, the message will be sent through the one, whose path from n to ń is the shortest .Number of edges in the shortest path connecting the two communicating MKCSSs and includes ę.Number of member users, who belong to all the MKCSSs located in the shortest path that includes Number of Association Edges  connecting the two communicating MKCSSs.We describe in this section a mechanism we developed for quantifying the Binary Influence (BI) of an Association Edge. That is, we quantify the extent to which the cross-members shared by two SACs relate the two SACs. The quantification is expressed in terms of a score that serves as an indicator of the ion Edge . The BI μ, ν) connecting two SACs Sμ and Sν. The equation quantifies the influence of the Association Edge  in passing information between each pair of nodes, one residing at Sμ and the other at Sν. We constructed the formula based on the generic notations shown in Sμ and Sν, respectively.Based on the above observations, we constructed the formula in BI: The Binary Influence of Association Edge , which connects SACs Sμ and Sν.Sμ: The SAC that contains MKCSS node μ.Sν: The SAC that contains MKCSS node ν.Sμ and Sν respectively.μ and ν respectively.μ in Sμ.ν in Sν.μ.ν.Sμ and Sν . Each of them is represented by an Association Edge that controls some of the information flow between the two SACs. Intuitively, the BI of each of these edges is impacted by the number of other Association Edges connecting the two SACs. The influence of the edge increases as the number of other Association Edges decreases. That is, the influence of the edge increases at the expense of the other edges. Let |μ ∩ ν| be the number of Association Edges connecting SACs Sμ and Sν . We need to adjust μ ∩ ν| increases. This is because as |μ ∩ ν| increases, the influence of each edge in controlling the flow of information between Sμ and Sν decreases. Reversibly, as |μ ∩ ν| decreases, the influence of each edge increases at the expense of the other edges. We adjusted As demonstrated by relative influence in passing information between SACs Sμ and Sν. Specifically, the adaptation of logarithm penalizes and rewards the Association Edge exponentially based on its degree of passing information between the two SACs relative to the other Association Edges. This helps in discriminating between an Association Edge among the following lists of Association Edges: (a) a list with varying very large number of Association Edges, and (b) a list with varying very small number of Association Edges. We adjusted We adjusted the formula in Sμ and Sν. Towards this, we aim at quantifying the collective influence of all the Association Edges that connect Sμ and Sν in passing information between the two SACs. That is, we adjusted the equation in such a way that BI computes one holistic BI score that reflects the collective Association Edges’ influence in passing information between the two SACs. We adjusted the equation accordingly as shown in Finally, we need to adjust the formula in Running Example 3: To better illustrate global relative influence in associating all SACs in the MRG. We constructed the formula in such a way that it quantifies the degree of influence of the Association Edge in terms of its average chance in passing information between the SACs at its end points and the remaining SACs in the MRG. That is, the formula computes the average chance of an Association Edge  connecting two SACs Sμ and Sν relative to the other Association Edges connecting the two SACs in passing information between: (a) Sμ or Sν, and (b) the remaining SACs in the MRG. The score is computed based on the BI of  as a fraction of the BIs of all Association Edges that pass information between: (a) Sμ or Sν, and (b) the remaining SACs in the MRG. By applying the above on the generic SACs in We heuristically developed a formula that assigns each Association Edge a GI score that characterizes its GI: The Global Influence (GI) score of the Association Edge  that connects SACs Sμ or SνBI: The BI score of the Association Edge .Sμ: The SAC containing MKCSS node μ.Sμ. SνSν: The SAC containing MKCSS node ν.Sν. N: Overall number of nodes that belong to SACs Sμ and Sνμ, ν) as the following ratio increases: (1) the number of shortest paths from each node n in SACs Sμ and Sν to the edge , to (2) the overall number of shortest paths from n to the other Association Edges connecting Sμ and Sν to the remaining SACs in the MRG. The rationale behind this is that the increase in the above ratio is an indicator of the centrality of the Association Edge  in controlling the flow of information between each of SACs Sμ and Sν and the remaining SACs in the MRG relative to the centralities of the other Association Edges that can pass the same flow of information. We adjusted the formula in We need to adjust the formula in Sμ to the Association Edge .Sμ to each other Association Edge Sμ to another SAC Sν to the Association Edge .Sν to each other Association Edge Sν to another SAC μ, ν), through which the information sent by the node passes. That is, the higher the weights of the nodes sending their information through the Association Edge , the more influential is the Association Edge. The rationale behind this is that as a node’s weight increases, its influence increases, which in turn reflects in the influence of the Association Edge, through which the information sent by the node passes. That is, as the collective influence of the MKCSS nodes that send their information through the Association Edge  increase, the influence of the  in controlling the flow of information throughout the MRG increases at the expense of the other Association Edges. Towards this, we adjusted the formula in We now need to consider the impact of nodes’ centralities on the centralities of the Association Edges connecting them. A node’s centrality is manifested by its weight . Intuitively, the higher the weight of an MKCSS node, the larger is the contribution of the node on the GI score of the Association Edge  the range of variations in large nodes’ weights, and (2) the range of variations in small nodes’ weights. We adjusted Finally, we aim at using the characteristics of logarithm to capture and further enhance the eigenvector observation/principle . That isRunning Example 4: To better illustrate Most often, some links in social networks are not revealed as a result of members who did not fully disclose and declare their memberships to all communities. It is imperative for uncovering such missing Association Edges prior to detecting cross-communities because they may contain valuable cross-membership information. First, ID_CC converts the MRG into a tree data structure for the ease of uncovering missing Association Edges connecting hierarchical interrelated SACs. A tree is a connected acyclic graph that reflects the hierarchical tree structure of the graph. The root of the tree is a parent node and is not a child of any node. Each node has only one parent and may have ancestor nodes. A leaf node is a node that does not have a child node. We call the resulting converted tree data structure the MKCSS Relationship Tree.ɳ and ɱ be two adjacent neighbouring nodes in the MRG. If ɱ is closer to the root node than ɳ, we consider ɱ to be the parent of ɳ in the MKCSS Relationship Tree. Thus, we construct the MKCSS Relation Tree by identifying the parent of each node ɳ as follows. From the set of Association Edges connected to ɳ, we select the Association Edge Ҿ with the highest GI score. If ɳ and ɱ are the end points of Ҿ, we consider ɱ to be the parent of ɳ. The rationale behind this is that, from among the set of nodes connected to ɳ, ɱ is the most closely associated with ɳ.We followed the following procedure for converting a MRG into a MKCSS Relationship Tree. Let Running Example 5: Consider our running MRG example shown in Ҿ that will be removed from the MRG because the two nodes at its end points are connected to other Association Edge, whose GI scores are higher than that of Ҿ. n1 and n2 be a node lca1. If: (1) some node lca2 is an ancestor of node lca1, (2) lca2 is the LCA of nodes n1 and n3, and (3) nodes n3 and n2 share some social characteristics, we can infer that node n3 is related to nodes n1. We call lca2 the RLCA of nodes n3 and n1.We propose the concept of Relevant Lowest Common Ancestor (RLCA) for uncovering missing  Association Edges. As plotted in n2 and n3 in n3 and n1 as depicted in By applying the concept of RLCA, we can uncover implicit Association Edges in the MRG as follows. If the characteristics shared between nodes Running Example 6: By applying the RLCA concept against the MKCSS Relationship Tree of our running example shown in We constructed an algorithm called RELPlookup  to efficDefinition 5—Dewey ID: A Dewey ID of a node n1 is a sequence of components. Each component is a sequence of digits separated by decimal points. Each component represents the Dewey ID of an ancestor node n2 of n1. The component to the left of the last decimal point of the Dewey ID of n1 is the parent node of n1. Dewey IDs are assigned to nodes based on the Depth First Search. When the sequence of components in the Dewey ID of n1 are read from left to right, they reveal the chain of ancestors of n1, starting from the root node. For example, consider the Dewey ID of SAC 1.1.1 in leaves, which contains the Dewey IDs of the leaf nodes in the tree. Each iteration of the algorithm produces a new stack state. Each stack entry has three array components , where: ENT is the entry of the node pushed into the stack, Leaf node is the Dewey ID of the leaf node pushed into the stack, and LCA-Leaf node is the pair of LCA and the leaf nodes passed to this LCA from entries popped out of the stack. If di, dj,…, and dk are the Dewey ID components in the stack from the bottom entry to the stack entry, then (1) the stack entry represents the SAC, whose Dewey ID is di, dj,…, and dk, and (2) the bottom entry represents the root SAC node, whose Dewey ID is di. The symbol q in line 7 of Algorithm RELPlookup represents the number of Dewey ID components of a pushed node that match the components in the entry of the current stack state. If q equals the number of Dewey components in the current stack state, the currently processed leaf node is a descendant of the priorly processed node. If this is the case, subroutine PushEntries  one of these LCAs is an ancestor of the other LCA, and (3) the ancestor and descendant LCAs contains the information of three leaf nodes. The three leaf nodes will be output as the result.The input to the algorithm is an array called hEntries  is callehEntries  is callehEntries  outputs Running Example 7: Let us apply algorithm RELPlookup  as shown in u and ν. After computing the BIs, the GIs of these implicit edges will be computed using the formula presented in To compute the GIs of the uncovered implicit Association Edges, we need to first compute their BIs. We need to adjust the BI formula in Running Example 8: As demonstrated in densest multi-profiled cross-SACs, to which an active user belongs. The larger the number of implicitly detected SACs, to which the active user belongs, the denser and more specific is the multi-profiled cross-community identified by the system for the user. ID_CC assigns the active user to the densest and most granular multi-profiled cross-SACs that matches his/her own social traits. Towards this, it employs a mechanism that can implicitly infer the SACs, to which the user belongs but were not revealed by the user. The mechanism adopted by ID_CC is based on applying the Maximum Spanning Tree (MaxST) algorithm [all Association Edges including the implicitly identified ones). All SAC nodes located in the path of the MaxST that connects the user’s revealed  SAC nodes, will be considered the comprehensive list of SACs, to which the user belongs. The extra SACs in the list (excluding the ones declared by the user) are implicitly identified. The resulting intersection of all SACs in the list is the densest and most granular multi-profiled cross-SACs that matches the user’s own social traits. This technique considers all cross-profiles that come to existence from the interrelations between overlapped social profiles. We construct the MaxST based on the GI scores of the Association Edges. A MaxST is a tree that spans all the SAC nodes in the MKCSS Relationship Tree. The sum of the GI scores of the Association Edges connecting the nodes is the largest among all other trees that span all the nodes. The MaxST can be computed using Kruskal’s algorithm [In this section, we describe the methodology adopted by ID_CC for inferring the lgorithm , 23 on tlgorithm  after muRunning Example 9: After multiplying the Association Edges’ GIs scores in our running MRG example shown in We aim at evaluating two features of our method ID_CC. The first one is ID_CC’s feature that detects the smallest cross-profiled cross-communities, to which an active user belongs. The second one is ID_CC feature that predicts missing implicit Association Edges in MRGs. We evaluated the first feature by comparing ID_CC with eight baseline models. Unfortunately, we could not find a comparable baseline model that predicts implicit Association Edges. Therefore, we evaluated the second feature as follows. We ran ID_CC against datasets that have no missing links. Then, we ran it against the same datasets but after removing some links. Finally, we compared the detected cross-communities before and after the links were removed.DNMF: It is a method proposed by Ye et al. [e et al.  based onLOCD: It is a method proposed by Ni et al. [S be a set of nodes that belong to at least two communities. The method selects a subset S′ ⊆ S as seed nodes. It identifies a community for each seed node. A node n′ is assigned to the same community of a seed node n, if the fuzzy relation between n′ and n is large enough.i et al.  for deteCoRel: It is a method proposed by Huang et al. [g et al.  for builRCF: It is a method proposed by Guesmi et al. [i et al.  that empNeo4j: It is a graph database engine that adopts the Property Graph model [ph model . A node GKS, BRWS, and GLPS: The three methods were proposed by Sharma et al. [GKS is an expansion of the Katz approach , which aBRWS is a method that adopts the group accretion concept. It uses semi-supervised learning and network alignment approaches to quantify the affinity among actors. By analysing cycles, the method determines the affinity of a group of users to an external actor. The method identifies the cycles that pass-through groups of nodes.GLPS is a method that employs semi-supervised and hypergraph label propagation approaches. The method diffuses labels by random walks. After the random walks are stabilized, the final labels of the external nodes are considered as affinity scores to the given groups.GKS assesses the affinity between an external actor and a group of users separately. On the other hand, BRWS assesses the affinity between an external actor and a subgroup within a group of users. The GKS method adopts an incremental accretion procedure that incrementally joins an external actor to an existing group. The BRWS method adopts a subgroup accretion procedure that considers the collaboration of external actors within a subset of an existing group.network of actors. On the other hand, the GLPS method is based on paths on a network of groups (NOG) by adopting label propagation score, which is based on hypergraph structure.The BRWS and GKS are based on paths and cycles over a The main differences between the three methods are summarized as follows:a et al. , as follWe aim at evaluating ID_CC’s accuracy for detecting cross-communities by comparing it with eight baseline methods. These methods employ nodes’ attribute information as the basis for detecting communities. Below are brief descriptions of the eight methods:https://github.com/teapot123/CoRel.The code of CoRel  is availhttps://github.com/ahollocou/multicom.The code of LOCD  is availhttps://github.com/smartyfh/DNMF.The code for DNMF  is availAs for GKS, GLPS, and BRWS, we used the same dataset and followed the same experimental setup employed for evaluating the three methods as described in Sharma et al. .The codes of the above methods are available as follows:http://134.209.27.183/ (see Appendix for how the demo works).We implemented ID_CC in Java, ran it in Intel(R) Core(TM) i7-6820HQ processor with 32 GB RAM and 2.70 GHz CPU under Windows 10 Pro. The demo application of ID_CC can be accessed through the following link: Տ be the set of communities in a dataset. For each different subset Ś ⊂ Տ, we determined the subset Ŋ of nodes resulting from the intersection of the communities in Ś . We aim at using Ŋ as a ground-truth cross-community resulting from the overlapping of the subset Ś. That is, we evaluated the accuracy of each method for detecting the cross-community resulting from the overlapping of each Ś ⊂ Տ by comparing its results with the subset Ŋ. Intuitively, a method’s accuracy may decrease as the number of overleaped communities increases. Therefore, we also aim at evaluating a method’s accuracy stability as the number of overleaped communities increases. Towards this, we computed the accuracy of each method for detecting each subset Ŋ that results from the overlapping of m different number of communities in the subset Ś. Specifically, we considered m = | Ś | = 2, 3, 4, 5, and 6. The m number of communities are selected randomly. In the case of ID_CC, we considered m as the number of a hypothetical user’s revealed number of communities.Let We evaluated the accuracies of the nine methods for detecting cross-communities in terms of Adjusted Rand Index (ARI) and F1-score measures. ARI computes the similarity of two clusters’ pair-wise comparisons. It is defined as:We use ARI to assess the pair-wise similarities between the cross-communities detected by one of the methods and a corresponding ground-truth cross-community. F1- score is the harmonic average of recall and precision. It is defined as: F1-score = 2  / .m number of communities . The DBLP dataset is a collection of real-world ground-truth networks put together by the Stanford Network Analysis Project (SNAP) . It inclThe same testing and training periods for the main splits. Splits are marked with fixed end years. Articles published in the years 2008 to 2010 were used for testing and articles published in the years 2004–2007 were used for training see .The same used dataset, which was the DBLP.top (IA)” and “Precision@Ntop (IA)” for Ntop = 100. These measures are defined as:The same measures used, which were “Recall@NTo substantiate the findings and outcome of the previous tests, we also evaluated the methods by employing the same experimental setup and same dataset used for evaluating BRWS, GKS, and GLPS in . This intop is the N top sorted IA set, IA is the incremental accretion, and Top-Ntop is the largest score in N top sorted IA set.Where, Nk number of communities . The Friendster dataset is a collection of real-world ground-truth networks put together by SNAP . The datk number of communities . The Facebook Social Circles dataset is a collection of real-world ground-truth networks put together by SNAP . It was In this test, we aim at evaluating the effectiveness of ID_CC to uncover missing (implicit) Association Edges. First, we ran ID_CC to build the MRGs of the DBLP, Friendster, and Facebook Social Circles datasets. Then, we randomly removed 500 Association Edges from the MRGs and evaluated the accuracy of ID_CC in identifying and reinstating these edges. We then repeated the same procedure nine times. In each of the nine times, we increased the number of removed Association Edges by 500. That is, the number of removed edges were 500, 1000, …, 5000. ID_CC’s accuracy of identifying the missing Association Edges is assessed by its accuracy in detecting the original communities in the datasets after reinstating these edges. Figs variation within a group to determine if the variation is large enough to be statistically significant. Since: (1) each group in our experiments represents the overlapping of different k number of communities , and (2) it is known that the accuracy decreases as k increases, we did not focus on the model that estimates the variation between groups. However, we did consider the variation between groups in the context of computing F-Statistic to determine how large the variability between group means compared to the variability of the observations within the groups. Due to the specific nature of our experiments, we want the variation among a group to be small while F-Statistic to be large. Tables We used One-way ANOVA Test  to deterThe experimental results demonstrated that ID_CC predicted cross-communities of nodes with multiple attributes with outstanding accuracy. The results showed that ID_CC outperformed the eight methods it was compared with. By observing the experimental results, we attribute the performance of ID_CC over the other methods, in general, to the combination of the following capabilities of ID_CC: (1) detecting granular multi-attributed cross-communities by analysing the hierarchical interrelationships and overlaps of single-attributed communities, (2) employing the novel concepts of MKCSS and MRG, which proved to produce effective graphical miniatures that depict communities and their ontological relationships, and (3) employing the novel concept of GI, which proved to be an effective mechanism for characterizing the global relative influences and interaction roles of Association Edges in MRGs.We observed from the experimental results that the accuracy of each of the nine methods decreases as the number of single-attributed communities, from which its detected cross-communities are constructed increases. However, ID_CC’s accuracy decreases in much smaller rate than the other eight methods as the number of communities, from which detected cross-communities are comprised increases. To confirm the above, we classified the cross-communities detected by each method into groups. Each group contains detected cross-communities comprised of the same number of single-attributed communities. We then computed the overall average ARI for each set as depicted in We attribute the above, mainly, to ID_CC’s concepts of MKCSS and MRG, which helped in locating cross-nodes regardless of the number of communities, to which these nodes belong. As a community grows smaller, its interests become more specific, which is manifested in the users’ profiles of the Facebook Social circles dataset. Finally, the constant enhancement of MRG contributed further to the performance of ID_CC. This is because, every time ID_CC detected a cross-community, it enhanced the MRG accordingly by incorporating newly detected missing Association Edges.k increased up to a certain value and then it kept declining thereafter. After investigating this phenomenon, we inferred the following: (1) increasing the value of k leads to enhancing MKCSS (as k increases the MKCSS keeps retaining only strongly associated nodes and discarding other nodes), and (2) as the value of k increases, the percentage of nodes that become non-member of any MKCSS increases . Specifically, we observed that the accuracy kept improving (inference (1)) until a certain value of k where the percentage of non-member nodes became large enough that resulted in degrading the accuracy (inference (2)). That is, the accuracy degrades as the percentage of non-member nodes increases. To confirm the above, we varied the value of k in the range 3–6. Under each different value of k, we performed the following: (1) computed the ARI of the results, and (2) computed the percentage of nodes that are non-member of any MKCSS. k.We observed from the experimental results that the value selected for k in k-clique had an impact on the accuracy of IC_CC. That is, ID_CC’s accuracy was to some degree k-dependent. We observed that its accuracy kept improving as the value of We observed that GKS inferred with acceptable accuracy cross-communities resulted from the overlapping of communities with rather high degree of common attribute homogeneity. However, the accuracy of its detection was poor for most of the cross-communities resulted from the overlapping of communities that exhibited high attribute heterogeneity. We attribute this limitation to GKS’s Katz score, which proved to be very ineffective in identifying the significant features of attributes, which would be employed for measuring the similarity between communities. The score is ineffective for determining the features in the profiles of communities that dictate their relationships. We also observed that the Katz score is sensitive to small differences between the profiles of an ego and an ‘alter’ in the Facebook Social Circle dataset. A small change between the profiles of an alter and an ego resulted in a large change in the Katz score. This was evident in scenarios where two alters have minor changes in their profiles, yet they achieved significantly different Katz scores. As a result, the score may mistakenly consider an alter as related to an ego.direct links between internal and external actors. However, the method had an inconsistency in measuring the affinity between external and internal target actors through indirect links. This is due to the fact that the bi-random walk technique adopted by the BRWS method has the limitation of random fluctuations when measuring the affinities between different external and internal actors through the indirect links connecting them. As a result, the method may produce misleading affinity scores.We observed that the BRWS method inferred with acceptable accuracy most of the cross-communities that exhibited too many We observed that GLPS inferred with good accuracy most of the cross-communities composed of nodes that have loose dependencies with one another. However, the hypergraph-based clustering technique employed by GLPS can cause dependencies between nodes. This resulted in many independent nodes. Moving these nodes to a cross-community required other dependent nodes to be moved with them, which is incorrect. Moreover, GLPS detected cross-communities that did not conform to tighter balancing constraints, which caused hyperedge to span several cross-communities. This cause the sizes of cross-communities to be incorrectly increased.The experimental results revealed that RCF was successful in building global sequences of context sets and their corresponding sequences of lattice sets from the datasets. This is attributed to the Concept Analysis technique adopted by RCF, which helped it in successfully converting the links between a dataset’s objects into attributes and inferring a set of lattices whose concepts are linked by relations. The method did so in relations consisted of a small to moderate number of instances of the relations. However, the method was not successful in inferring sets of lattices, whose concepts are linked by relations consisted of many instances of the relations. The key limitation of RCF is that it considers only a limited number of quantifiers. It performed miserably in relations required a combination of quantifiers not in the considered set.The experimental results revealed that the property graph technique employed by Neo4j was effective in scenarios where edges and nodes possess different types of meta-information. We observed that the Neo4j’s pattern matching of nodes while traversing a graph contributed significantly to the accuracy of cross-communities inferred by the method. This is due to Neo4j’s effective path-oriented queries and Cypher’s declarative graph query language. The method proved to be effective in clustering complex networks  into accurate cross-communities. This is because the ecosystem and its associated functionality on top of Neo4j helped it in storing infinite levels of community overlaps. By analyzing the experimental result, we deduced the following limitations of Neo4j: (1) it allowed only one label per edge and one value per attribute property whereas some datasets have multiple labels and values, (2) Cypher adopts no-repeated-edge semantics, and (3) it had indexing limitations, especially for edges annotated with attribute terms.The accuracy results achieved by DNMF were satisfactory overall. After investigating the results, we deduced this performance to DNMF’s adoption of the mutual guidance of the following two information types: (1) guidance information learnt through a unified manner: pseudo supervision module (which adopts unsupervised procedure for uncovering discriminative information), and (2) guidance information learnt through community memberships. By observing the experimental results, we found that DNMF obtained good results when the tradeoff parameter was not assigned large values. The method did not achieve good results when: (a) the size of values assigned to it was large, (b) the number of overlapping memberships was large, or (c) the number of overlapping nodes was large. The method exhibited good execution time. We attribute this to the algorithm employed by the method, which decomposes the objective function into independent subproblems without the need for post-processing.By analyzing the experimental results, we found that the accuracy of LOCD kept improving as the number of seed nodes selected by the method increased. This is because as the number of seed nodes increases, the number of nodes bordering the seed nodes and share characteristics similar to the detected communities increases. We also found that LOCD’s number of accurately selected seed nodes increases as the fuzzy relation threshold increases. Specifically, we found that LOCD inclined to obtain good results after setting the fuzzy relation threshold to at least 0.87 . The method outperformed most of the other methods in inferring cross-communities that contain some nodes belonging to disconnected subnetworks. The Facebook Social Circles and Friendster datasets exhibited many of such disconnected subnetworks. We attribute this to the local community detection technique employed by LOCD, which helps in overcoming the problem of missing global information in disconnected subnetworks. The major limitation of LOCD stems from the fact that its detection accuracy is highly dependent on parameters’ setting.Overall, CoRel achieved outstanding accuracy results. We attribute this, mainly, to the methodology it employs for constructing taxonomies to detect the related terms of each concept. By observing the experimental results, we found that CoRel successfully inferred the related terms associated with a community’s profile/property terms for many concepts. It extracted distinctive terms for many network nodes effectively. We deduced that the co-clustering procedure adopted by the method to ignore inconsistent subtopics played a significant role in its outstanding performance. The major limitation of CoRel stems from its enriching procedure, which did not effectively enforce term distinctiveness in a number of networks.densest holonic ones with various adaptive multi-social profiles, because they exhibit many interesting properties. Unfortunately, methods that stress the detection of granular multi-profiled cross-communities have been under-researched. Most current methods detect multi-profiled communities without consideration to their granularities. To overcome this, we introduced in this paper a novel methodology for detecting the smallest and most granular multi-profiled cross-community, to which an active user belongs. The methodology is implemented in a system called ID_CC. The proposed system considers all cross-profiles that come to existence from the interrelations between overlapped social profiles (both known and implicitly inferred overlaps). It employs the novel concepts of MKCSS and MRG, which proved to produce effective graphical miniatures of communities and their ontological relationships. It also employs the novel concept of GI, which proved to be an effective mechanism for characterizing the global relative influence and interaction role of Association Edges in networks.The most important types of such multi-profiled cross-communities are the There are always new users wishing to join cross-communities that match their own social traits. ID_CC detects cross-community in such a way that it matches a new user’s own social traits. The larger the number of inferred user’s communities, the denser and more specific is the multi-profiled cross-community identified by the system for the user. Towards this, ID_CC implicitly infers an active user’s undeclared and unknown communities that match his own social traits using novel techniques. It detects cross-communities by analysing hierarchically overlapped social profiles to infer all cross-profiles that come to existence from the interrelations between the communities. It detects the densest multi-profiled cross-communities from heterogeneous social networks.densest and most granular multi-profiled cross-communities, to which an active user belongs, (2) assesses the binary and global influences of the links connecting community nodes using novel mechanisms, (3) infers missing links prior to detecting cross-communities using novel mechanisms, and (4) employs novel graphical miniatures that depict communities and their ontological relationships.To the best of our knowledge, this is the first work that: (1) analyses hierarchically overlapped social profiles to detect the hierarchical interrelationships of single-attributed communities using the concepts of MKCSS, MRG, and GI played a considerable role in the quality of cross-communities detected by ID_CC. We observed from the results that the value selected for k in k-clique had an impact on the accuracy of IC_CC. That is, ID_CC’s accuracy was to some degree k-dependent. We will investigate approaches for overcoming this limitation in a future work.We evaluated ID_CC by comparing it experimentally with the following eight methods: CoRel , LOCD 2, DNMF 2, RCF 2828, Neo4jS1 Appendix(DOCX)Click here for additional data file.


The results demonstrated that individuals with high tendencies toward PIPU showed enhanced attentional engagement with pornographic stimuli in the early stage of attentional processing (100 ms), followed by attentional avoidance in the late stages of attentional processing (500 ms). Moreover, the severity of PIPU symptoms was positively correlated with attentional engagement scores in the short picture-time trials (100 ms) and weakly negatively correlated with attentional disengagement scores in the long picture-time trials (500 ms). This approach–avoidance pattern of attentional biases is in line with a recent theoretical model that emphasizes that appetitive and aversive motivational processes jointly determine attentional bias.Attentional bias plays a vital role in the occurrence and development of addictive behaviors. However, little is known about attentional processes in problematic Internet pornography use (PIPU), and previous studies have reported mixed results. The current study examined the components of attentional processing to sexual stimuli using an exogenous cueing task designed to differentiate between attentional engagement and disengagement. Two different stimulus presentation times (100 and 500 ms) were used to present the pornographic and neutral images to differentiate the early and late stages of attentional bias. Individuals with high ( In the past few decades, the consumption of online pornography has increased exponentially . An onliAlthough the classification of PIPU remains controversial, several scholars believe that it should be conceptualized as behavioral addiction, which contains the key ingredients for addiction, such as withdrawal, tolerance, loss of control, and compulsive use –8. ThereThe current cognitive model of addiction suggests that enhanced attentional capture of addiction-related cues plays a vital role in the persistence of addictive behaviors . That isAccording to Posner's attentional processing model , three tTo date, few studies have investigated attention-related cognitive processing in PIPU. One of the most commonly used methods to assess attentional bias is the Stroop task. In this task, participants are required to identify the color of the words while ignoring their semantic content. It is assumed that slower color naming on trials with the addiction-related words indicates attentional processing of the meaning of the words, which interferes with color naming. Using the modified addiction Stroop task , individCollectively, the available evidence seems to suggest that individuals with PIPU may exhibit biased attentional processing in two ways: (a) enhanced attentional engagement  and (b) enhanced attentional disengagement . Unfortunately, no studies have examined the nature of sex-related attentional bias in PIPU. The assessment instruments mentioned in the above studies do not allow a direct distinction between attentional engagement with a cue and attentional disengagement from this cue. These instruments tend to yield an overall indicator of attentional bias, in which engagement and disengagement processes are intertwined.To examine the attentional components involved in PIPU, the present study employed an exogenous cueing task  that canThe following hypotheses were tested in the present study:  participMage = 19.69, SD = 1.77) were recruited from a pool of 273 male college students who had previously completed the Problematic Internet Pornography Use Scale . Each iThe participants sat approximately 60 cm in front of a monitor. Each trial started with a central fixation point flanked by two empty squares and was presented for 1,000 ms. Subsequently, a picture cue was displayed in one of the boxes for 100 or 500 ms. Then, the cue disappeared, and after 50 ms, the target (a black square) appeared in the left or right box. Participants were tasked to determine the location of the target as quickly and accurately as possible, pressing the “1” key on a standard computer keyboard when the target appeared on the left and the “2” key when the target appeared on the right. The target disappeared after the key was pressed, and the longest presentation time was 2,000 ms .The task consisted of 12 practice trials and 192 experimental trials . Two-thirds of the experimental trials  were congruent , and one-third  were incongruent . Each picture was presented four times, as congruent and incongruent trials, for both 100 and 500 ms. Picture cues and targets appeared with the same probability on the left or right box. All trials were presented in random order.Incorrect responses  were excluded from analysis. In line with previous studies , reactiot-test. To examine the differences between individuals with high and low PIPU tendencies in attentional bias for pornographic stimuli, a 2 × 2 × 2 × 2 mixed-design analysis of variance (ANOVA) was performed, with Stimulus Type , Congruency (congruent/incongruent), and Stimulus Duration (100/500 ms) as within-subjects variables, and Group (high/low PIPU) as the between-subjects variable. A one-sample t-test was used for attentional engagement and disengagement to test whether they were significantly different from zero. Partial correlation analysis  was used to examine the relationship between attentional bias and PIPU scores.Group differences in age, PIPUS score, depression, and anxiety were analyzed using an independent sample This study was conducted in accordance with the Declaration of Helsinki, and approved by the local ethics committee. The participants provided written informed consent after they understood the contents of the experiment.t(78) = 14.06, p < 0.001, Cohen's d = 3.14. Moreover, the high PIPU tendency group was more depressed and anxious than the low PIPU tendency group, as indicated by the SDS, t(78) = 2.74, p = 0.008, Cohen's d = 0.61, and SAS scores, t(78) = 2.30, p = 0.024, Cohen's d = 0.51, respectively.Group characteristics are presented in t-test was conducted for each attentional bias index to test whether these indices were significantly different from zero. Individuals with high tendencies toward PIPU exhibited significantly stronger attentional engagement with pornographic pictures than with neutral pictures in the short image time trials, t(39) = 4.00, p < 0.001, Cohen's d = 0.63. Furthermore, individuals with low tendencies toward PIPU showed impaired disengagement from pornographic pictures than from neutral pictures in the long image time trials, t(39) = 2.61, p = 0.013, Cohen's d = 0.41. No other attentional bias indices were significantly different from zero.The mean and SD of the RTs based on group and trial are shown in r = 0.36, p = 0.001) and weakly negatively correlated with attentional disengagement in the long picture time trials .After controlling for RTs to neutral cues, partial correlations between the attentional bias indicators and PIPUS scores were performed. The results showed that PIPU symptoms were positively correlated with attentional engagement scores in the short picture time trials  than incongruent cues , F = 297.34, p < 0.001, F = 5.30, p = 0.024, F = 5.18, p = 0.026, F = 6.54, p = 0.012, F = 14.47, p < 0.001, F = 9.17, p = 0.003, A 2 (Group) × 2  × 2 (Congruency) × 2 (Stimulus Duration) repeated-measures ANOVA on RTs revealed a significant main effect of congruency, with faster responses to the congruent cues  = 4.33, p = 0.041, F = 7.75, p = 0.008, M = 347.06 ms, SD = 43.80) than after neutral cues , F = 15.98, p < 0.001, M = 406.72 ms, SD = 52.71) did not differ for neutral cues , F = 0.02, p > 0.89. For the low tendencies toward PIPU group, the Stimulus Valence × Congruency interaction was not significant, F = 0.09, p > 0.76.At this stimulus duration, a Group × Stimulus Valence × Congruency interaction was found, ures see . In the F = 4.20, p = 0.044, F = 0.00, p > 0.96. For the low tendencies toward PIPU group, the Stimulus Valence × Congruency interaction was significant, F = 7.13, p = 0.011, M = 393.79 ms, SD = 51.25) than after neutral cues , F = 6.83, p = 0.013, M = 362.66 ms, SD = 50.27) did not differ for neutral cues , F = 0.45, p > 0.50.Again, a Group × Stimulus Valence × Congruency interaction was found, The primary objective of the present study was to investigate the differences between individuals with high and low PIPU tendencies in their attentional bias to sex-related and neutral images over time during an exogenous cueing task. To examine the nature of sex-related attentional bias in PIPU, attentional engagement and disengagement scores were measured over time. These attentional indices revealed that individuals with high PIPU tendencies were significantly more vigilant for pornographic images than for neutral images, relative to individuals with low tendencies toward PIPU at first (100 ms). However, over time (500 ms), individuals with high tendencies toward PIPU were significantly less attentive to pornographic images than those with low tendencies toward PIPU. Moreover, partial correlation analysis showed that the severity of PIPU symptoms was positively correlated with attentional engagement scores in the short picture-time trials (100 ms) and weakly negatively correlated with attentional disengagement scores in the long picture-time trials (500 ms). To our knowledge, this is the first study to distinguish between attentional engagement and disengagement when examining attentional bias toward sexual cues in the context of PIPU.The findings of this study suggest that the attentional bias mechanism that underlies PIPU may not be a simultaneous but a sequential process . This finding agrees with previous results that individuals with PIPU exhibit a stronger attentional bias to sexual cues relative to healthy controls when the stimulus presentation time is 150–220 ms , 29, wheThe presence of an attentional bias to sexual stimuli presented for 100 ms, followed by the absence of attentional bias at 500 ms in individuals with high PIPU tendencies, is also compatible with the vigilance–avoidance pattern of visual attention for addiction-related stimuli in substance use disorder [see ]. For exRecently, Field et al.  proposedConsidering the role of motivational conflict may explain the inconsistent results of previous attentional bias studies in the field of PIPU. Previous studies using the dot-probe task found that individuals with PIPU exhibited a stronger attentional bias to sexual cues relative to healthy controls when the stimulus presentation time was 150 ms , whereasThis study has several important theoretical and clinical implications. First, CSBD is currently classified as an impulse control disorder in the ICD-11. However, many scholars have suggested that it should be considered a behavioral addiction e.g., , 52]. Th. Th52)].This study had several limitations. First, the sample for this study consisted of college students who were identified using a self-reported scale. Future studies should replicate the results of this study by using clinical samples. Second, the RT indicators derived from the exogenous cueing task in this study provided an indirect measure of attentional bias. Further studies should employ more direct measures of attentional bias . Third,This is the first study to examine attentional bias toward sex-related stimuli between individuals with high and low PIPU tendencies with an exogenous cueing task designed to differentiate between attentional engagement and disengagement. This study provides the first evidence of an approach–avoidance pattern of attentional bias toward pornographic stimuli in individuals with high PIPU tendencies. More specifically, individuals with high tendencies toward PIPU exhibited an automatic attentional preference for sex-related stimuli with high incentive salience, followed by attentional avoidance at the late stage of attentional processing, which reflects a strategic process of reducing subjective discomfort. In addition, our data showed that the approach–avoidance pattern of attentional bias was significantly related to the severity of PIPU symptoms.The raw data supporting the conclusions of this article will be made available by the authors, without undue reservation.The studies involving human participants were reviewed and approved by Chengdu Medical College. The patients/participants provided their written informed consent to participate in this study.JW and YH contributed to the conceptualization and supervision. JW contributed to the formal analysis and writing—original draft. YH contributed to the writing—review and editing.This work was supported by grants from the Sichuan Sex Sociology and Sex Education Research Center (SXJYB2212), the Disciplinary Construction Innovation Team Foundation of Chengdu Medical College (CMC-XK-2105), Sichuan Social Sciences Planning Fund Program (SC20EZD010), and the Foundation of Sichuan Psychological Society (SCSXLXH2021042).The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article, or claim that may be made by its manufacturer, is not guaranteed or endorsed by the publisher.