Overlapping community detection in social networks

The growing importance of social media and networking has increased the efforts in this area. Social networks are structures formed by the communities which are came together. The main common feature of all kind of social networks is community structures. In real network structures, an element is likely to be included in multiple groups and this situation is called as overlapping.In this paper, we have two methods for solving the problem of identifying overlapping groups. According to the first method, social network was modeled as a graph and each fully connected subgraphs in this graph has been accepted as a community. Bron-Kerbosch algorithm has been applied to the adjacency matrix of social network modelled as graph and all maximal cliques in undirected graphes has been found. Then, with the suggested method, these maximal cliques was revised so that overlapping communities could be found. In another method, the social network is modeled as a graph again. The Laplacian matrix of graph is calculated and divided into two groups according to its eigenvalues and eigenvectors. Then, the possibility of elements being in two groups is identified by applying the minimum cutting edges process.