Community Discovery Based On Users’ Explicit And Implicit Relationship

Online Social Network is a network between people who use this network for social,collaboration and information sharing. Nowadays, the research on online social network isin full swing with the successful of Facebook, Twitter, Wechat and other large-scale onlinesocial network applications.Similar to social network, online Social Network also has community structure. Wecan evaluate current system, design the future online social network system and helpsystem operators to organize their own portal effectively by exploring the communitystructure in Online Social Network. On the other hand, community discovery can help theprovider of product and service to find other User groups accurately and rapidly toadvertising Targeted.In this paper, we propose a novel approach of community discovery based on users’explicit and implicit relationships. Firstly, We study users’ interest and preference andconstruct users’ profile based on users’ behavior. According to the users’ profile, we cancalculate similarity between users. If the similarity exceeds some given threshold, we canconsider there exists some implicit relationship between users. Then, we use the users’implicit relationships to extend the explicit network. Finally, we use an algorithms todetect the community structure of the extended network based on local modularitymeasure. Extensive experimental study on some real life social network dataset—Flickrsocial network dataset and Sina Micro-blog dataset—demonstrates that our approach iseffective for community discovery.