Mobile social networks, which consist of mobile users who communicate with each other using cell phones are reflections of people＇s interactions in social lives. Discovering typed communities （e.g., family communiti...Mobile social networks, which consist of mobile users who communicate with each other using cell phones are reflections of people＇s interactions in social lives. Discovering typed communities （e.g., family communities or corporate communities） in mobile social networks is a very promising problem. For example, it can help mobile operators to determine the target users for precision marketing. In this paper we propose discovering typed communities in mobile social networks by utilizing the labels of relationships between users. We use the user logs stored by mobile operators, including communication and user movement records, to collectively label all the relationships in a network, by employing an undirected probabilistic graphical model, i.e., conditional random fields. Then we use two methods to discover typed communities based on the results of relationship labeling： one is simply retaining or cutting relationships according to their labels, and the other is using sophisticated weighted community detection algorithms. The experimental results show that our proposed framework performs well in terms of the accuracy of typed community detection in mobile social networks.展开更多
基金supported by the Fundamental Research Funds for the Central Universities of Chinathe National Natural Science Foundation of China under Grant No. 60905029the Beijing Natural Science Foundation under Grant No. 4112046
文摘Mobile social networks, which consist of mobile users who communicate with each other using cell phones are reflections of people＇s interactions in social lives. Discovering typed communities （e.g., family communities or corporate communities） in mobile social networks is a very promising problem. For example, it can help mobile operators to determine the target users for precision marketing. In this paper we propose discovering typed communities in mobile social networks by utilizing the labels of relationships between users. We use the user logs stored by mobile operators, including communication and user movement records, to collectively label all the relationships in a network, by employing an undirected probabilistic graphical model, i.e., conditional random fields. Then we use two methods to discover typed communities based on the results of relationship labeling： one is simply retaining or cutting relationships according to their labels, and the other is using sophisticated weighted community detection algorithms. The experimental results show that our proposed framework performs well in terms of the accuracy of typed community detection in mobile social networks.
