Directed mixed membership stochastic blockmodel
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Abstract (arXiv)
Mixed membership modeling for undirected networks has been extensively explored in network science over the past few years. Despite the substantial progress made for undirected cases, handling mixed membership structures in directed networks continues to pose substantial difficulties. To address this gap, we introduce the Directed Mixed Membership Stochastic Blockmodel (DiMMSB), a novel framework tailored for directed networks with overlapping communities. A key feature of DiMMSB is its ability to treat the row and column nodes of the adjacency matrix as distinct entities, each potentially following its own community organization. Building on this model, we develop DiSP, an efficient spectral procedure to estimate mixed memberships for both sets of nodes. Through delicate analysis, we derive node-specific error bounds of DiSP under mild sparsity conditions. Simulation results support the theoretical results, demonstrating that DiSP achieves lower error rates and faster computation than its competitor. Moreover, applications to real data highlight DiSP's effectiveness in uncovering asymmetric structural patterns.
Key facts. Cited by 2 papers in the map (data through 7 Oct 2026). Builds on 17 of its 50 references that are in the map. First posted on arXiv 7 Jan 2021.
Builds on
17 of 50 references.
References · 17
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- 2013Consistency of spectral clustering in stochastic block modelscited by 128
- 2020Spectral Methods for Data Science: A Statistical Perspectivecited by 111
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- 2017Estimating Mixed Memberships with Sharp Eigenvector Deviationscited by 22
- 2017Mixed Membership Estimation for Social Networkscited by 21
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- 2015Convexified Modularity Maximization for Degree-corrected Stochastic Block Modelscited by 16
- 2016On Mixed Memberships and Symmetric Nonnegative Matrix Factorizationscited by 14
5 more
- 2013Semidefinite Programming Based Preconditioning for More Robust Near-Separable Nonnegative Matrix Factorizationcited by 13
- 2018Overlapping Clustering Models, and One (class) SVM to Bind Them Allcited by 10
- 2018Optimal Bipartite Network Clusteringcited by 8
- 2020Randomized spectral co-clustering for large-scale directed networkscited by 7
- 2017Matched bipartite block model with covariatescited by 1
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Citing papers per year: 2021: 1; 2022: 1.
2 papers.