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Overlapping Community Detection in Static and Dynamic Social Networks

Networks can be extracted from a wide range of real systems, such as online social networks, communication networks and biological systems. Detection of cohesive groups in these graphs, primarily based on link information, is the goal of community detection. Community structures emerge when a group of nodes is more likely to be linked to each other than to the rest of the network. The modules found can be disjoint or overlapping. Another relevant feature of networks is the possibility to evolve over time. Furthermore, nodes can have valuable information to improve the community detection process. Hence, in this work we propose to design a soft overlapping community detection method for static and dynamic social networks with node attributes. Preliminary results on a toy network are promising.