Resume Mining of Communities in Social Network

Recently there has been considerable interest in the study of community detection in social network. However, to get more detailed knowledge about the global organization of the whole network and the discovered communities, how to explain and utilize these communities will be far more significant in many practical scenarios. Thus, we propose the problem of resume mining of social network communities. We also study three important aspects of resume mining: the characterization of community , the discrimination among communities and the community evolution mining. Unlike other similar algorithms, our solutions fully consider the inner topology of a community together with the attributes of nodes. Then we also study two cases: the first is about mobile call graphs and the second is about co-authorship networks. The result shows that the community resume found by our methods represents state and history of a community clearly.