Abstract Community structure is one of the most prominent features of complex networks. Community structure detection is of great importance to provide insights into the network structure and functionalities. Most proposals focus on static networks. However, finding communities in a dynamic network is even more challenging, especially when communities overlap with each other. In this article, we present an online algorithm, called OLCPM, based on clique percolation and label propagation methods. OLCPM can detect overlapping communities and works on temporal networks with a fine granularity. By locally updating the community structure, OLCPM delivers significant improvement in running time compared with previous clique percolation techniques. The experimental results on both synthetic and real-world networks illustrate the effectiveness of the method.
@cite_27 proposed AFOCS, an adaptive framework for detecting, updating and tracing the evolution of overlapping communities in dynamic mobile networks. During the initialisation step, AFOCS identifies all possible basic network communities which represent the densely connected part of the network, whose internal density is greater than a certain level, and merge those with the highest overlaps with each other. In a second step, AFOCS adaptively update the community structure, as the dynamic network evolves in time.
@cite_27
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Many practical problems on Mobile networking, such as routing strategies in MANETs, sensor reprogramming in WSNs and worm containment in online social networks (OSNs) share an ubiquitous, yet interesting feature in their organizations: community structure. Knowledge of this structure provides us not only crucial information about the network principles, but also key insights into designing more effective algorithms for practical problems enabled by Mobile networking. However, understanding this interesting feature is extremely challenging on dynamic networks where changes to their topologies are frequently introduced, and especially when network communities in reality usually overlap with each other. We focus on the following questions (1) Can we effectively detect the overlapping community structure in a dynamic network? (2) Can we quickly and adaptively update the network structure only based on its history without recomputing from scratch? (3) How does the detection of network communities help mobile applications? We propose AFOCS, a two-phase framework for not only detecting quickly but also tracing effectively the evolution of overlapped network communities in dynamic mobile networks. With the great advantages of the overlapping community structure, AFOCS significantly helps in reducing up to 7 times the infection rates in worm containment on OSNs, and up to 11 times overhead while maintaining good delivery time and ratio in forwarding strategies in MANETs. Detecting the time evolution of the community structure of networks is crucial to identify major changes in the internal organization of many complex systems, which may undergo important endogenous or exogenous events. This analysis can be done in two ways: considering each snapshot as an independent community detection problem or taking into account the whole evolution of the network. In the first case, one can apply static methods on the temporal snapshots, which correspond to configurations of the system in short time windows, and match afterward the communities across layers. Alternatively, one can develop dedicated dynamic procedures so that multiple snapshots are simultaneously taken into account while detecting communities, which allows us to keep memory of the flow. To check how well a method of any kind could capture the evolution of communities, suitable benchmarks are needed. Here we propose a model for generating simple dynamic benchmark graphs, based on stochastic block models. In them, the time evolution consists of a periodic oscillation of the system's structure between configurations with built-in community structure. We also propose the extension of quality comparison indices to the dynamic scenario. The discovery of evolving communities in dynamic networks is an important research topic that poses challenging tasks. Evolutionary clustering is a recent framework for clustering dynamic networks that introduces the concept of temporal smoothness inside the community structure detection method. Evolutionary-based clustering approaches try to maximize cluster accuracy with respect to incoming data of the current time step, and minimize clustering drift from one time step to the successive one. In order to optimize both these two competing objectives, an input parameter that controls the preference degree of a user towards either the snapshot quality or the temporal quality is needed. In this paper the detection of communities with temporal smoothness is formulated as a multiobjective problem and a method based on genetic algorithms is proposed. The main advantage of the algorithm is that it automatically provides a solution representing the best trade-off between the accuracy of the clustering obtained, and the deviation from one time step to the successive. Experiments on synthetic data sets show the very good performance of the method when compared with state-of-the-art approaches. In this paper, a novel framework is proposed to jointly optimize user association and resource allocation in the uplink cognitive femtocell network (CFN). In the considered CFN, femtocell base stations (FBSs) are deployed to serve a set of femtocell user equipments (FUEs) by reusing subchannels used in a macrocell base station (MBS). The problem of joint user association, subchannel assignment, and power allocation is formulated as an optimization problem, in which the goal is to maximize the overall uplink throughput while guaranteeing FBSs overloading avoidance, data rate requirements of the served FUEs, and MBS protection. To solve this problem, a distributed framework based on the matching game is proposed to model and analyze the interactions between the FUEs and FBSs. Using this framework, distributed algorithms are developed to enable the CFN to make decisions about user association, subchannel allocation, and transmit power. The algorithms are then shown to converge to a stable matching and exhibit a low computational complexity. Simulation results show that the proposed approach yields a performance improvement in terms of the overall network throughput and outage probability, with a small number of iterations to converge.