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.


Marine microplastic pollution (MMP) is becoming one of the most pressing environmental problems facing humanity today. The novel coronavirus epidemic has raised the issue of environmental contamination caused by large-scale improper disposal of medical waste such as disposable masks (DMs). To assess the impact of MMP caused by DMs and to seek solutions for the prevention and control of MMP, this study uses the Driving force-Pressure-State-Impact-Response (DPSIR) framework to establish a causal chain of MMP caused by DMs. The conclusion shows that the novel coronavirus epidemic has led to a surge in the use of DMs, which has brought pressure on resource constraints and environmental pollution at the same time. Improperly DMs enter the environment and eventually transform into MMP, which not only endangers the marine ecological system but also poses potential human health risks as well as economic and social hazards. In addition, further research on environmentally friendly masks (cloth masks and biodegradable masks) is essential to mitigate the environmental damage caused by the large-scale global use of DMs. This study provides a scientific and theoretical basis for the assessment of MMP from discarded DMs, and the findings of this study will provide a reference for the formulation of relevant policies. The emergence of the novel coronavirus COVID-19) disease with strong contagiousness has attracted global attention since December 2019 9 disease,4. SinceDMs are mainly made of polypropylene (PP), which is considered plastic waste after being discarded. Its entry into the environment can cause plastic pollution and release microplastics, severely threatening the aquatic environment and aquatic life once they enter freshwater and coastal areas . The datSeveral studies have focused on the MMP induced by the rapid growth of waste in DMs. Sangkham measured the generation of DMs and medical waste in Asia during the outbreak and discussed the emerging challenges in solid waste management during and after the pandemic . Jung etIn order to fill this gap, this study analyzes and explores the problem of MMP caused by DMs from a multidimensional perspective, including social, economic, and environmental perspectives. The structured approach provided by the DPSIR framework is used to examine, in detail, the interrelations among the five components to better understand the current complex pollution situation of marine microplastics and the potentially significant pollution risks in the future. First, the driving forces of DM usage are discussed. Second, the social and environmental pressures created to meet human demand are examined. Then, the physical, chemical, and quantity states of the DM contaminants and the impact of their resulting impacts on marine ecosystems, human health and socioeconomics are examined. Next, responses to regulate the driving forces, mitigate the pressures and states, and impacts of MMP are reviewed from both upstream and downstream perspectives. Finally, specific recommendations for preventing and controlling MMP from DMs are derived.MMP is a complex environmental problem that requires an adaptive management framework that is action-oriented, integrated with an approach which is interdisciplinary in nature, incorporating both social and ecological systems . The DPSThe DPSIR framework is one of the key conceptual frameworks that are now widely used in marine environmental issues. It has been applied to various marine environmental issues, such as social-ecological systems in coastal environments , social According to the EEA , the comWith the widespread use of the DPSIR framework in environmental studies, the definition of the five components of the framework and the relationships among them have been explained in more detail. (1)The “driving forces” are the most primitive factors that cause environmental change and refer to the social, economic and institutional changes that directly or indirectly trigger environmental stress. It can be defined as “social, demographic, socio-economic development and corresponding changes in lifestyles, as well as overall consumption levels and production patterns” . Rodrigu(2)The “pressures” refer to anthropogenic factors that are directly applied to environmental systems by driving forces and contribute to environmental change, “including emissions of material, physical and biological agents, and the use of resources and space by human activities” . Pressur(3)The “states” indicate both qualitative and quantitative measurements of pollutants or the current state of the environment or environmental parameters that may change due to pressures . It is w(4)The “impacts” refer to the feedback effects of natural system states on human health, socioeconomics, and ecosystems, which often arise from adverse environmental conditions and are the result of a combination of the first three elements . Impacts(5)The “responses” refer to social actions taken by humans to prevent, mitigate, or eliminate negative impacts , which iThe DPSIR framework is used to address the MMP from DMs, following a typical five-step procedure . In thisThe COVID-19 pandemic has severely impacted the health and livelihood of people worldwide. DMs have a great filtering effect on aerosols and droplets, which can prevent respiratory diseases. Wearing DMs effectively reduces the probability of being infected by novel coronavirus , which mTo avoid the crisis of economic downturn and communication blockage brought about by precautionary measures such as lockdowns and travel restrictions, people in different countries choose to wear DMs on a large scale, while maintaining social distancing, keeping good hand hygiene (hand washing), and avoiding public or crowded spaces as alternative measures to keep society functioning properly .Since 2020, health authorities in different countries have issued various recommendations for using face masks to protect against COVID-19, which tend to be dynamically adjusted  32]. Wh. Wh32]. The raw materials of DMs are mostly made of petroleum-based polymer-PP (PP) fiber, which is a non-renewable resource. Therefore, the massive use of DMs can cause a large consumption of natural resources. Furthermore, DMs are used in a wide range of applications and scenarios. They are likely to be directly discarded in municipal solid waste without safe disposal measures or directly in the environment. This creates new challenges for medical waste management, which will worsen especially for developing countries with inadequate waste management . Discarded DMs are found scattered in public places such as parks and streets all over the world . A studyThe DMs are made of melt-blown fabric, non-woven fabric, mask straps, and nose clips. Melt-blown fabric is the core material used to produce DMs, whose main component is PP. The melt-blown cloth consists of ultra-fine fibers with diameters of about 1–5 microns arranged randomly to form numerous tiny pores that trap dust due to the electrostatic effects. When the droplets containing various types of viruses approach the PP melt-blown fabric, they can be adsorbed on the surface of the melt-blown fabric and cannot penetrate. Thus, DM has a strong filtration property, sufficient to reject bacteria, suspended particles, liquid droplets, and aerosols. The inner layer, near the mouth and nose, is made of hot-rolled nonwoven fabric to absorb moisture and ensure that the wearer’s lips are dry and comfortable. The entire mask is designed to fold to improve its fit to the wearer’s face, blocking more than 90% of the 5μm particles. The structure of DMs varies slightly between standards, with disposable surgical masks and N95 masks having a high acceptance rate among health workers and the public . Their sExperimental results show that individual N95 masks and disposable surgical masks have approximately 11 and 4.5 g of PP and other plastic derivatives , respectively ,47. BecaThe use of DMs has reduced the spread of the virus in society while simultaneously causing severe environmental problems in terms of microplastic pollution. During the use of DMs, extremely fine nonwoven fibers tend to break off and form microplastics. Mechanical wear and tear between the fibers is further exacerbated by the repeated use of DMs over time, leading to extra microplastics. In addition, DMs may intercept microplastics in the air during use, thus increasing their microplastic carrying capacity . 21 units of microplastic into the environment [Discarded DMs enter the ocean through improper landfills and dumping treatment. They disintegrate into tiny plastic fragments under a range of physical and chemical effects, such as solar radiation, biological erosion, low-temperature weathering, tidal, wave washing, and so on. As the DMs gradually age and decompose in the environment, the entire DM will become completely microplastic and enter the ocean . Shen etironment . Using kironment . The avaUnlike other plastic products such as plastic bags and bottles, DMs are more likely to be damaged and decomposed in the natural environment, releasing additional microplastic fibers . The melDiscarded DMs entering the environment may not only entangle the bodies of marine wildlife but also release microplastics that are preyed upon by marine life, thus affecting normal growth and reproduction . ExperimMicroplastics have been found in organisms at different levels of the marine food chain ,62. The With the increasing amount of marine organisms caught and seafood supplied by aquaculture year by year, microplastic particles in these marine organisms and aquaculture products may eventually enter the human food chain. The pollution status of the marine environment by microplastic is a potential health problem for humans. It has been shown that harmful microorganisms could attach to plastic debris , increasFoods made from marine organisms, such as fish, are an important food source for humans. MMP raises concerns about global food security, which may eventually lead to the consequences of food shortages . In the To respond to the impact of MMP from DMs, especially the impact on marine ecosystems, the potential impact on human health, and the impact on the economy and society, a series of prevention and control strategies have been developed at different levels. According to the principles of source reduction and process management of MMP prevention and control , the resIn the context of the normalization of epidemic prevention and control, DMs have become an immediate need in people’s daily lives, and their demand cannot be reduced in the short term . The fun(1) Improving public participation in the proper disposal of DMsGuiding the public to properly dispose of DMs is the most effective way to properly prevent the problem of microplastic pollution at the source. Used DMs can greatly reduce the risk of environmental contamination after proper disposal such as disinfection and incineration ,78. At p(2) Strengthen the management of DM wasteDue to the disposable natures of DMs, recycling as a reduction pathway to achieve more limited results, and scientific management based on the disposal process will be sought to reduce the risk of MMP caused by DMs. The COVID-19 pandemic has increased the pressure on municipal waste management, which can lead not only to the spread of the virus but also to environmental problems if not handled properly . TherefoCurrently, several medical waste bins with clearly marked points are placed in public areas such as hospitals and communities to collect discarded DMs. They are packaged in double-layered medical waste bags and disposed of as general medical waste by specific personnel, municipal solid waste workers, and special waste management departments of companies . (3) Using cloth masks or biodegradable masks as an alternativeThe promotion of cloth masks can effectively reduce the consumption of DMs ,45. ClotThe development of biodegradable masks is another solution idea to reduce the risk of the DMs contamination. The advantage of biodegradable materials is their biodegradability through composting, which avoids environmental hazards at the source to some extent. The biodegradable polymer and bio-based material electrospun ensemble can be used as the main material of masks. Experiments revealed that this type of mask reaches 98% particle filtration efficiency during use, and its waste can be biodegraded within four weeks . In addiThe downstream responses mainly focus on recycling and reusing discarded DMs to reduce the risk of MMP. Medical plastic waste generated during the epidemic has a high calorific value compared to other conventional fuels, accounting for nearly 25% of the calorific value of municipal solid waste . Discard(1) Physical-mechanical recyclingImproving the process of mechanical recycling of DMs can produce the recycled product, recycled polyethylene, with better stiffness and strength, which enables physical and mechanical recycling ,91. The (2) Chemical recycling2, CH4, and CO, which suggests an environmentally benign disposal process [Multi-stage pyrolysis was used to catalyze the cracking of discarded DMs to generate H process ,93. Xian process . TherefoDMs, as a new type of microplastic pollutant, can continuously release microplastics into the marine environment through the water systems. However, the rigidity of their demand and single-use nature makes it difficult to establish closed-loop chain management of the entire life cycle such as source reduction, waste process control, and end-of-life recycling. This study is based on the analytical framework of DPSIR, which seeks more effective management measures to curb the MMP prevention and control brought by discarded DMs. The results of the study show the surge in demand for DMs’ uses under the three driving forces of human health risk, social demand, and mask policy promotion in the context of the COVID-19 epidemic, increased the risk of non-renewable resources such as oil tension, plastic waste increase, and microplastic flow into the environment, thus increasing the pressure on both resources and the environment. In addition, the physical-chemical composition of discarded DMs leads to waste ending up in the ocean in the form of large amounts of microplastics. In order to effectively reduce the impact on the marine ecosystem, human health, and socioeconomics caused by MMP from DMs, response mechanisms are proposed for upstream and downstream in a targeted manner.In view of this, countries around the world should guide the public on the proper disposal of DMs and educate the public on the environmental responsibilities that individuals should assume. Secondly, it is necessary to strengthen the management of DM waste and improve the infrastructure to help the public dispose of DMs reasonably. In addition, a system of rewards and penalties for recycling DMs could be developed as a complement to fully incentivize the public to dispose of DMs reasonably. Moreover, eco-friendly alternatives such as cloth masks and biodegradable masks with filtration efficiency that meet basic protection needs and have a longer life cycle can be developed. In addition, further research into the risks of MMP from DMs is essential, and new technologies should be developed to overcome the problems associated with the release of microplastics from DMs.