Finding research community in collaboration network with expertise profiling

Authors:
Hao Wu;Jun He;Yijian Pei;Xin Long
Affiliations:
School of Information Science and Engineering, Yunnan University, Kunming, P.R. China;School of Electronic and Information Engineering, Nanjing University of Information Science and Technology, Nanjing, P.R. China;School of Information Science and Engineering, Yunnan University, Kunming, P.R. China;School of Information Science and Engineering, Yunnan University, Kunming, P.R. China
Venue:
ICIC'10 Proceedings of the 6th international conference on Advanced intelligent computing theories and applications: intelligent computing
Year:
2010

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Abstract

As a new task of expertise retrieval, finding research communities for scientific guidance and research cooperation has become more and more important. However, the existing community discovery algorithms only consider graph structure, without considering the context, such as knowledge characteristics. Therefore, detecting research community cannot be simply addressed by direct application of existing methods. In this paper, we propose a hierarchical discovery strategy which rapidly locates the core of the research community, and then incrementally extends the community. Especially, as expanding local community, it selects a node considering both its connection strength and expertise divergence to the candidate community, to prevent intellectually irrelevant nodes to spill-in to the current community. The experiments on ACL Anthology Network show our method is effective.