COMMUNITY DETECTION IN ONLINE SOCIAL NETWORKS USING ACTIONS OF USERS

Recently, the online social networks provide a rich resource of Heterogeneous data which its analysis can lead to discover unknown information and relations within such networks. In analysis of social networks data, a challenging issue is the discovery of community including "similar" nodes and it has widely been studied in the social networking community in the context of the structure of the underlying graphs. The online social networks, additionally having graph structures, include effective information of users within networks, which using this information can lead to improve the quality of communities' discovery. In this paper, instead of using centrality measures in social networks analysis, to discover leaders and similar nodes, user actions are used and by using these leaders, communities are identified. First, based on Interests and activities of users in networks, we discover some small communities of similar users, and then by using social relations, extend (those) communities. Finally, by conducting doing empirical studies, the efficiency of our approach on community discovery within the online social networks will be demonstrated.