COMMUNITY DETECTION IN SOCIAL NETWORKS USING FREQUENT PATTERN MINING
Subject Areas : Information Technology in Engineering Design (ITED) JournalSeyed Ahmad Mosavi 1 , Mehrdad Jalali 2 , Negin Misaghian 3
1 - Computer group
2 - Computer group
3 - Computer Group
Keywords:
Abstract :
recently, the website social networks provide a rich resource of Heterogeneous data which the analysis of these data can lead to discover unknown information and relations in these networks. The discovery of community including “similar” nodes is a challenging issue in analysis of social networks data, and it has widely 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, for detecting community, a method is provided that in addition to the communication among nodes, to improve the quality of the discovered communities, content information is used, as well. This method is a new approach based on a frequent pattern and the actions of users in the network and in particular, is performed on social networking sites where users select their favorite acts. 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. The F-measure evaluated results on two real-world datasets (Blogcatalog and Flicker) demonstrate that the proposed method leads to improvethe quality of community detection.
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