Exploring the structural regularities in networks

Abstract

In this paper, we consider the problem of exploring structural regularities of networks by dividing the nodes of a network into groups such that the members of each group have similar patterns of connections to other groups. Specifically, we propose a general statistical model to describe network structure. In this model, group is viewed as hidden or unobserved quantity and it is learned by fitting the observed network data using the expectation-maximization algorithm. Compared with existing models, the most prominent strength of our model is the high flexibility. This strength enables it to possess the advantages of existing models and overcomes their shortcomings in a unified way. As a result, not only broad types of structure can be detected without prior knowledge of what type of intrinsic regularities exist in the network, but also the type of identified structure can be directly learned from data. Moreover, by differentiating outgoing edges from incoming edges, our model can detect several types of structural regularities beyond competing models. Tests on a number of real world and artificial networks demonstrate that our model outperforms the state-of-the-art model at shedding light on the structural features of networks, including the overlapping community structure, multipartite structure and several other types of structure which are beyond the capability of existing models.

8 pages, 5 figures

References in corpus (18)

• Modularity and community structure in networks
• Uncovering the overlapping community structure of complex networks in nature and society
• Finding community structure in networks using the eigenvectors of matrices
• Cooperative Game Theory Approaches for Network Partitioning
• Hierarchical structure and the prediction of missing links in networks
• Stochastic blockmodels and community structure in networks
• An information-theoretic framework for resolving community structure in complex networks
• Detect overlapping and hierarchical community structure in networks
• Mixture models and exploratory analysis in networks
• An efficient and principled method for detecting communities in networks
• Quantifying and identifying the overlapping community structure in networks
• Spectral methods for the detection of network community structure: a comparative analysis
• Bridgeness: A Local Index on Edge Significance in Maintaining Global Connectivity
• Uncovering the community structure associated with the diffusion dynamics of networks
• Covariance, correlation matrix and the multi-scale community structure of networks
• Inversion method for content-based networks
• Triangular clustering in document networks
• Population stratification using a statistical model on hypergraphs

Cited by in corpus (11)

• Efficiently inferring community structure in bipartite networks
• Cumulative Effect in Information Diffusion: A Comprehensive Empirical Study on Microblogging Network
• Stochastic block model and exploratory analysis in signed networks
• Overlapping community detection in signed networks
• Exponential random graph models for networks with community structure
• Self-falsifiable Hierarchical Detection of Overlapping Communities On Social Networks
• Automatic exploration of structural regularities in networks
• InfoNCE is a Free Lunch for Semantically guided Graph Contrastive Learning
• A Generative Model for Exploring Structure Regularities in Attributed Networks
• On Equivalence of Likelihood Maximization of Stochastic Block Model and Constrained Nonnegative Matrix Factorization
• Identification of hybrid node and link communities in complex networks