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The emergence of online social networks has revolutionized millions of web users’ behavior so that their interactions with each other produce huge amounts of data on different activities. Community detection, herein, is one of the most important tasks. The very recent trend is to detect meaningful communities based on users’ interactions or the activity network. However, in many of such studies, authors consider the basic network model while almost ignoring the model of the interactions in the multi-layer network. In this research, an experimental study is done to compare community detection in Facebook friendship network to that of activity network, considering different activities in Facebook OSN such as sharing. Then, a new community detection evaluation metric based on homophily is proposed. Eventually, a new method of community detection based on different activities in Facebook social network is presented. In this method, we generalized three familiar similarity methods, Jaccard, Common Neighbors and Adamic-Adar for multi-layered network model.

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