| Summary: | 碩士 === 國立中央大學 === 資訊管理研究所 === 100 === There has been much recent research about identifying communities in networks. Based on the online social network, which is getting more and more popular recently, we explore the community detection problem, i.e., how to identify the hidden sub-groups in the heterogeneous social network.
Traditional research on community detection usually assumed that the structural information of the network is fully known, which is not feasible for many practical networks. Moreover, most previous algorithms for community detection did not differentiate multiple relations existing among objects or persons in a real world. In Facebook, two persons can be either friend or not friend. But in reality a friend relation may come from different reasons and belong to different social groups. Thus, how to differentiate different relations among users on Facebook is a key research issue in our work. In this paper, we propose a new approach utilizing the social interaction data, rather than structural information of the network, to address the community detection problem in Facebook. Specifically, we develop a method to find the multiple social groups of a Facebook user from his/her past interaction data with friends. The advantages of our approach include: i) it does not depend on the structural information, ii) it can differentiate different relations existing among friends, iii) it allows a friend belonging to multiple communities at the same time. In the experiment, we retrieve 10 Facebook user’s data as our datasets and evaluate the performance of each dataset. The results show that our method can identify the hidden social groups of users successfully from the interaction data in Facebook. Experimental results verify the feasibility and effectiveness of our approach.
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