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Learning community structures: global and local perspectives

Learning community structures: global and local perspectives

Tang, Xianchao, Xu, Tao, Feng, Xia, Yang, Guoqing, Wang, Jing, Li, Qiannan, Liu, Yanbei and Wang, Xiao (2017) Learning community structures: global and local perspectives. Neurocomputing, 239. pp. 249-256. ISSN 0925-2312 (doi:https://doi.org/10.1016/j.neucom.2017.02.026)

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Abstract

Uncovering community structures is a fundamental and important problem for analyzing complex networks. The topology information, as the direct representation of networks, is widely used for community detection. But in fact, there are other two important types of information related with network topology: the global information which captures the importance of nodes in the whole network, and the local information which describes the similarities between nodes. It is of great value to consider the information of individual nodes and information between them for community detection methods simultaneously, which is largely ignored by previous methods. In this work, we integrate the global and local information uniformly in a novel nonnegative matrix factorization (NMF) based model. Specifically, in the global aspect, we employ the PageRank to derive the importance of nodes, so that the more important the node is, the more influence the node is in the network. In the local aspect, we utilize nearness between nodes to obtain the similarities between nodes, so that nodes with larger similarities will have similar community memberships. Thereafter, we derive the multiplicative updating rule to learn the model parameter. Numerous experiments demonstrate that our approach has gained performance improvements up to almost 5% in comparison with the state-of-the-art methods.

Item Type: Article
Uncontrolled Keywords: community detection, topology information, global information, local information, nonnegative matrix factorization
Subjects: Q Science > QA Mathematics > QA75 Electronic computers. Computer science
Faculty / Department / Research Group: Faculty of Liberal Arts & Sciences
Faculty of Liberal Arts & Sciences > School of Computing & Mathematical Sciences (CAM)
Last Modified: 26 Apr 2021 15:19
Selected for GREAT 2016: None
Selected for GREAT 2017: None
Selected for GREAT 2018: None
Selected for GREAT 2019: None
Selected for REF2021: None
URI: http://gala.gre.ac.uk/id/eprint/30502

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