Exploring the roles of cannot-link constraint in community detection via Multi-variance Mixed Gaussian Generative Model

• Published in: PloS one Vol. 12; no. 7; p. e0178029
• Main Authors: Yang, Liang, Ge, Meng, Jin, Di, He, Dongxiao, Fu, Huazhu, Wang, Jing, Cao, Xiaochun
• Format: Journal Article
• Language: English
• Published: United States Public Library of Science 05.07.2017; Public Library of Science (PLoS)
• Subjects: Algorithms; Analysis; Analysis of variance; Biology and Life Sciences; Coding; Collaboration; Communities; Community Networks; Computer and Information Sciences; Computer networks; Computer science; Design and construction; Earth Sciences; Engineering; Factorization; Gaussian process; Gaussian processes; Humans; Information Dissemination - methods; Laboratories; Management; Methods; Models, Theoretical; Multivariate Analysis; Network topologies; Nodes; Normal Distribution; People and Places; Physical Sciences; Publishing; Research and Analysis Methods; Social Behavior; Social Media - utilization; Social Networking; Social networks; Social Sciences; Society; Studies; Topology; Variance; Beijing China; China
• ISSN: 1932-6203; 1932-6203
• DOI: 10.1371/journal.pone.0178029

Due to the demand for performance improvement and the existence of prior information, semi-supervised community detection with pairwise constraints becomes a hot topic. Most existing methods have been successfully encoding the must-link constraints, but neglect the opposite ones, i.e., the cannot-link constraints, which can force the exclusion between nodes. In this paper, we are interested in understanding the role of cannot-link constraints and effectively encoding pairwise constraints. Towards these goals, we define an integral generative process jointly considering the network topology, must-link and cannot-link constraints. We propose to characterize this process as a Multi-variance Mixed Gaussian Generative (MMGG) Model to address diverse degrees of confidences that exist in network topology and pairwise constraints and formulate it as a weighted nonnegative matrix factorization problem. The experiments on artificial and real-world networks not only illustrate the superiority of our proposed MMGG, but also, most importantly, reveal the roles of pairwise constraints. That is, though the must-link is more important than cannot-link when either of them is available, both must-link and cannot-link are equally important when both of them are available. To the best of our knowledge, this is the first work on discovering and exploring the importance of cannot-link constraints in semi-supervised community detection.