Community extraction for social networks

Abstract

Analysis of networks and in particular discovering communities within networks has been a focus of recent work in several fields, with applications ranging from citation and friendship networks to food webs and gene regulatory networks. Most of the existing community detection methods focus on partitioning the entire network into communities, with the expectation of many ties within communities and few ties between. However, many networks contain nodes that do not fit in with any of the communities, and forcing every node into a community can distort results. Here we propose a new framework that focuses on community extraction instead of partition, extracting one community at a time. The main idea behind extraction is that the strength of a community should not depend on ties between members of other communities, but only on ties within that community and its ties to the outside world. We show that the new extraction criterion performs well on simulated and real networks, and establish asymptotic consistency of our method under the block model assumption.

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• Coauthorship and Citation Networks for Statisticians
• Network histograms and universality of blockmodel approximation
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• Stochastic blockmodel approximation of a graphon: Theory and consistent estimation
• Finding multiple core-periphery pairs in networks
• Automated Delineation of Hospital Service Areas and Hospital Referral Regions by Modularity Optimization
• A Survey on Theoretical Advances of Community Detection in Networks
• Co-clustering separately exchangeable network data
• Ubiquitousness of link-density and link-pattern communities in real-world networks
• Detecting Overlapping Communities in Networks Using Spectral Methods
• Statistical Modelling of Citation Exchange Between Statistics Journals
• A testing based extraction algorithm for identifying significant communities in networks
• A generalised significance test for individual communities in networks
• Community extraction in multilayer networks with heterogeneous community structure
• A framework for statistical network modeling
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• Bayesian estimation of the latent dimension and communities in stochastic blockmodels
• Node mixing and group structure of complex software networks
• Spectral Algorithms for Community Detection in Directed Networks
• The blessing of transitivity in sparse and stochastic networks
• Network Cross-Validation for Determining the Number of Communities in Network Data
• Social Network Fusion and Mining: A Survey
• Local community extraction in directed networks
• Testing Community Structures for Hypergraphs
• Pairwise Covariates-adjusted Block Model for Community Detection
• Scalable Spectral Algorithms for Community Detection in Directed Networks
• Optimal Bayesian estimation in stochastic block models
• Profile Likelihood Biclustering
• Universal Rank Inference via Residual Subsampling with Application to Large Networks
• Non Parametric Statistics of Dynamic Networks with distinguishable nodes
• Local multiresolution order in community detection
• Spectral clustering in the dynamic stochastic block model
• Probabilistic community detection with unknown number of communities
• Detecting Statistically Significant Communities
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• Community Detection Based on the $L_{\infty }$ convergence of eigenvectors in DCBM
• Finding compact communities in large graphs
• Corrected Bayesian information criterion for stochastic block models
• Sampling node group structure of social and information networks
• Mathematical Modeling of Systemic Risk in Financial Networks: Managing Default Contagion and Fire Sales
• Distributed Pseudo-Likelihood Method for Community Detection in Large-Scale Networks
• Network induced large correlation matrix estimation
• Logistic Regression Augmented Community Detection for Network Data with Application in Identifying Autism-Related Gene Pathways
• Community Detection by $L_0$-penalized Graph Laplacian