Enhanced semi-supervised community detection with active node and link selection

Abstract

Semi-supervised community detection has gained a lot of attention by leveraging side information for better understanding network topologies. However, most of existing works select side information in a random manner. They usually require a great amount of side information to significantly improve the performance of community detection. Besides, they have to define the number of communities in advance. To address these issues, this paper proposed an active semi-supervised community detection method, called SK-rank-D. The key advantages of this framework are twofold: (1) Actively selecting a small amount of links as side information to“sharpen” the boundaries between communities and “compact” the connections within communities; (2) Automatically referring the number of communities by selecting informative nodes in communities. Empirical analysis on both synthetic and real-world networks showed the effectiveness and rationality of the newly proposed method in deciding community number. We also compared with competing semi-supervised community detection methods, the experimental results demonstrated the superior performance of our approach.