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Recent developments in the internet and technology have made majoradvancements in tools that facilitate the collection of social data, opening upthus new opportunities for analyzing social networks. Social network analysisstudies the patterns of social relations and aims at discovering the hiddenfeatures embedded in the structure of social networks. One of the mostimportant features in social networks is community structure : densely knitgroups of individuals. The dynamic nature of interaction in social networksoften challenges the detection of such community structures. The contributionsin this thesis fall into two categories.The first category highlights theproblem of identifying overlapping communities over time. To carry out suchanalysis, a framework called OLCPM (Online Label propagation and CliquePercolation Method) is proposed. It is an online algorithm based on cliquepercolation and label propagation methods. OLCPM has two main features : thefirst one is its ability to discover overlapping communities, while the secondis its effectiveness in handling fine-grained temporal net works. As for as thesecond category is concerned, it emphasizes on the problem of analyzingcommunities that are embedded at different temporal scales. For example, innetworks of interaction such as e-mails or phone calls, individuals areinvolved in daily as well as occasional conversations. We propose a firstmethod for analyzing communities at multiple temporal scales. Hence, thedynamic network (link streams) is studied at different temporal granularities,and coherent communities (called stable communities) over a period of time aredetected at each temporal granularity. The two proposed approaches arevalidated on both synthetic and real-world datasets.

An Approach for Detecting Dynamic Communities in Social Networks