A hybrid method for community detection based on user interactions, topology and frequent pattern mining

In recent years, community detection in social networks has become one of the most important research areas. One of the ways to community detection is to use interactions between users. There are different types of interactions in social networks, which, if used together with network topology, improve the precision of community identification. In this paper, a new method based on the combination of user interactions and network topology is proposed to community detection. In the community formation stage, the effective nodes are identified based on eigenvector centrality, and the primary communities around these nodes are formed based on frequent pattern mining. In the community expansion phase, small communities expand using modularity and the degree of interactions among users. To calculate the degree of interaction between users, a new measure based on the local clustering coefficient and interactions between common neighbors is proposed, which improves the accuracy of the degree of user interactions. Analysis of Higgs Twitter and Flickr datasets utilizing internal density metric, NMI and Omega demonstrates that the proposed method outperforms the other five community detection methods