An efficient semi-supervised community detection framework in social networks

Zhen Li, Yong Gong, Zhisong Pan, Guyu Hu, Sergio Gómez
2017 PLoS ONE  
Community detection is an important tasks across a number of research fields including social science, biology, and physics. In the real world, topology information alone is often inadequate to accurately find out community structure due to its sparsity and noise. The potential useful prior information such as pairwise constraints which contain must-link and cannot-link constraints can be obtained from domain knowledge in many applications. Thus, combining network topology with prior
more » ... to improve the community detection accuracy is promising. Previous methods mainly utilize the must-link constraints while cannot make full use of cannot-link constraints. In this paper, we propose a semi-supervised community detection framework which can effectively incorporate two types of pairwise constraints into the detection process. Particularly, must-link and cannot-link constraints are represented as positive and negative links, and we encode them by adding different graph regularization terms to penalize closeness of the nodes. Experiments on multiple real-world datasets show that the proposed framework significantly improves the accuracy of community detection.
doi:10.1371/journal.pone.0178046 pmid:28542520 pmcid:PMC5441628 fatcat:mtqddf5jyrgltnkmm5upskayeq