Resume Mining of Communities in Social Network Export

@inproceedings{citeulike:2891201, abstract = {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.}, author = {Wu, Bin and Pei, Xin and Tan, Jianbin and Wang, Yi}, booktitle = {Data Mining Workshops, 2007. ICDM Workshops 2007. Seventh IEEE International Conference on}, citeulike-article-id = {2891201}, citeulike-linkout-0 = {http://dx.doi.org/10.1109/ICDMW.2007.130}, citeulike-linkout-1 = {http://ieeexplore.ieee.org/xpls/abs_all.jsp?arnumber=4476704}, doi = {10.1109/ICDMW.2007.130}, journal = {Data Mining Workshops, 2007. ICDM Workshops 2007. Seventh IEEE International Conference on}, keywords = {algorithm, clustering, community\_evolution, graphs, social\_networks}, pages = {435--440}, posted-at = {2008-06-13 13:00:43}, priority = {0}, title = {Resume Mining of Communities in Social Network}, url = {http://dx.doi.org/10.1109/ICDMW.2007.130}, year = {2007} }

TY - CONF ID - citeulike:2891201 L3 - citeulike-article-id:2891201 N2 - 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. JF - Data Mining Workshops, 2007. ICDM Workshops 2007. Seventh IEEE International Conference on EP - 440 TI - Resume Mining of Communities in Social Network T2 - Data Mining Workshops, 2007. ICDM Workshops 2007. Seventh IEEE International Conference on SP - 435 KW - algorithm KW - clustering KW - community_evolution KW - graphs KW - social_networks AU - Wu, Bin AU - Pei, Xin AU - Tan, Jianbin AU - Wang, Yi PY - 2007/// UR - http://dx.doi.org/10.1109/ICDMW.2007.130 ER -