Stochastic block model and exploratory analysis in signed networks

Abstract

We propose a generalized stochastic block model to explore the mesoscopic structures in signed networks by grouping vertices that exhibit similar positive and negative connection profiles into the same cluster. In this model, the group memberships are viewed as hidden or unobserved quantities, and the connection patterns between groups are explicitly characterized by two block matrices, one for positive links and the other for negative links. By fitting the model to the observed network, we can not only extract various structural patterns existing in the network without prior knowledge, but also recognize what specific structures we obtained. Furthermore, the model parameters provide vital clues about the probabilities that each vertex belongs to different groups and the centrality of each vertex in its corresponding group. This information sheds light on the discovery of the networks' overlapping structures and the identification of two types of important vertices, which serve as the cores of each group and the bridges between different groups, respectively. Experiments on a series of synthetic and real-life networks show the effectiveness as well as the superiority of our model.

12 pages, 8 figures, 2 tables. arXiv admin note: text overlap with arXiv:1110.1976 by other authors

References in corpus (16)

• Uncovering the overlapping community structure of complex networks in nature and society
• Finding community structure in networks using the eigenvectors of matrices
• Comparing community structure identification
• Hierarchical structure and the prediction of missing links in networks
• Statistical Mechanics of Community Detection
• Stochastic blockmodels and community structure in networks
• Quantifying social group evolution
• Multirelational Organization of Large-scale Social Networks in an Online World
• Missing and spurious interactions and the reconstruction of complex networks
• Mixture models and exploratory analysis in networks
• Community detection in networks with positive and negative links
• Phase transition in the detection of modules in sparse networks
• Analysis of community structure in networks of correlated data
• On network bipartivity
• Role models for complex networks
• Mesoscopic analysis of networks: applications to exploratory analysis and data clustering