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— Community detection is a vital research area for online social networks. Since there is not a formal context in users` profiles, a new data source of user`s attributes is extracted from online social networks. Then in this paper a novel algorithm is investigated. Both attributes and relations of users are used in the proposed algorithm; therefor e communities can be detected through users’ similar characteristics or users common relationships. The experiments show that the accuracy of the algorithm is comparable to other well-known algorithms; moreover detected communities are self-descripted through the mode of each community members. Keywords — Social networks, Community detection, Influential nodes, Self-descriptive communities. I. Introduction Social network community detection is an important issue for means of effective advertisements, accurate recommender systems and tracking changes. Since social networks consist of several data sources, they can be very informative that yield self-descriptive communities. Through the community detection process, it is possible to categorize common relations between users and analyse each related part of a network, community, separately in more detail. Changes can also be shown by tracking communities. First studies on community detection methods focused on link analysis. Since those methods failed to extract semantic of the communities, hybrid methods are favored recently. These methods enhance the efficiency of community detection results [1-4]. Almost all of these methods use Bayesian models, and they combine two algorithms or two data sources. These data sources are often links and context. Using context as a data source has some limitations that are listed below. Handling context as a data source for community detection is a voluminous task that needs algorithms with high computational complexities; therefore, the majority of research studies extract the most frequent words or keywords from the context, and then use these limited words as the content. Furthermore, Contexts are extracted from scientific papers written with correct grammatical instructions and formal vocabularies. In the case of online social networks

Relation and Attribute Fusion to Detect Communities of Online Social Networks