li-semisupervised-2019.pdf (4.63 MB)
Semi-Supervised Community Detection via Constraint Matrix Construction and Active Node Selection
journal contribution
posted on 2020-01-01, 00:00 authored by Suqi Zhang, Junyan Wu, Jianxin LiJianxin Li, Junhua Gu, Xianchao Tang, Xinyun XuIdentification of community structures is essential for characterizing and analyzing complex networks. Having focusing primarily on network topological structures, most existing methods for community detection ignore two types of non-topological relationships among nodes, i.e., pairwise “must-link” constraints among pairs of nodes and labels of nodes, such as functions they may have. Here, we present a novel semi-supervised and active learning method for community detection to integrate these two types of information of a network so as to increase the accuracy of community identification. Our new method will honor the “must-link” relationship without introducing new parameters and is efficient with a guaranteed convergence. An essential component of the method is a linear representation that is particularly suited to an active learning to help select the most critical nodes that impact community discovery. We present results from extensive experiments on synthetic and real networks to show the superior performance of the new methods over the existing approaches.
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Journal
IEEE AccessVolume
8Pagination
39078 - 39090Publisher
Institute of Electrical and Electronics Engineers (IEEE)Location
Piscataway, N.J.Publisher DOI
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ISSN
2169-3536eISSN
2169-3536Language
engPublication classification
C1 Refereed article in a scholarly journalUsage metrics
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