Hidden community detection in social networks

被引:64
|
作者
He, Kun [1 ,2 ]
Li, Yingru [1 ]
Soundarajan, Sucheta [3 ]
Hoperoft, John E. [2 ]
机构
[1] Huazhong Univ Sci & Technol, Wuhan, Hubei, Peoples R China
[2] Cornell Univ, Ithaca, NY 14850 USA
[3] Syracuse Univ, Syracuse, NY USA
基金
中国国家自然科学基金;
关键词
Community detection; Hidden community; Structure mining; Social networks; COMPLEX NETWORKS;
D O I
10.1016/j.ins.2017.10.019
中图分类号
TP [自动化技术、计算机技术];
学科分类号
0812 ;
摘要
This paper introduces a new graph-theoretical concept of hidden community for analysing complex networks, which contain both stronger or dominant communities and weak communities. The weak communities are termed as being with the hidden community structure if most of its members also belong to the stronger communities. We propose a meta approach, namely HICODE (Hidden COmmunity DEtection), for identifying the hidden community structure as well as enhancing the detection of the dominant community structure. Extensive experiments on real-world networks are carried out and the obtained results demonstrate that HICODE outperforms several state-of-the-art community detection methods in terms of uncovering both the dominant and the hidden structure. Due to the difficulty of labeling all ground truth communities in real-world datasets, HICODE provides a promising technique to pinpoint the existing latent communities and uncover communities for which there is no ground truth. Our finding in this work is significant to detect hidden communities in complex social networks. (C) 2017 Elsevier Inc. All rights reserved.
引用
收藏
页码:92 / 106
页数:15
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