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This paper proposes a statistical generative model for community detection in networks with node attributes. It separates node activity into independent topological and attribute correction factors, and reports gains over existing methods, especially when graph structure is weak. The results come from synthetic and real networks, so readers should check how well they transfer to their own graphs before adopting the approach.
Dual-Corrected Stochastic Block Model ($\text{DC}^2\text{SBM}$): A Generalized Structure Detection Method for Attributed Networks
arXiv:2610.40676v1 Announce Type: cross Abstract: Community detection in attributed networks is one of the core tasks of complex network analysis. The stochastic block model (SBM) and its variants are able to detect generalized network structures such as assortative, disassortative and mixed structures. However, existing generative methods either ignore node heterogeneity, or characterize node activity in the topological and attribute dimensions by one and the same factor, making it difficult to correctly model node activity when the two kinds of activity are decoupled. In this paper, we propose the Dual-Corrected Stochastic Block Model ($\text{DC}^2\text{SBM}$), which introduces independent correction factors---a topological degree factor and an attribute abundance factor---into the link-generation and attribute-generation channels, respectively, thereby effectively decoupling node activity from membership inference, and which further introduces a global scaling parameter to resolve the mismatch between the normalization constraints and the network scale. The model is inferred by the expectation--maximization (EM) algorithm, and the newly introduced factors cancel out completely in the posterior computation, so that no additional computational cost is incurred. Experiments on synthetic and real attributed networks show that DC^2SBM outperforms state-of-the-art methods in community detection accuracy, with an especially pronounced advantage when the topological signal is weak. The two correction factors can be recovered independently from their respective channels without cross-talk. Analytical as well as controlled experiments further demonstrate that modeling with a single shared factor fails when the two kinds of activity are decoupled---a regime that is prevalent in real networks---thereby validating the necessity of dual independent correction.
Abstract:Community detection in attributed networks is one of the core tasks of complex network analysis. The stochastic block model (SBM) and its variants are able to detect generalized network structures such as assortative, disassortative and mixed structures. However, existing generative methods either ignore node heterogeneity, or characterize node activity in the topological and attribute dimensions by one and the same factor, making it difficult to correctly model node activity when the two kinds of activity are decoupled. In this paper, we propose the Dual-Corrected Stochastic Block Model ($\text{DC}^2\text{SBM}$), which introduces independent correction factors---a topological degree factor and an attribute abundance factor---into the link-generation and attribute-generation channels, respectively, thereby effectively decoupling node activity from membership inference, and which further introduces a global scaling parameter to resolve the mismatch between the normalization constraints and the network scale. The model is inferred by the expectation--maximization (EM) algorithm, and the newly introduced factors cancel out completely in the posterior computation, so that no additional computational cost is incurred. Experiments on synthetic and real attributed networks show that DC^2SBM outperforms state-of-the-art methods in community detection accuracy, with an especially pronounced advantage when the topological signal is weak. The two correction factors can be recovered independently from their respective channels without cross-talk. Analytical as well as controlled experiments further demonstrate that modeling with a single shared factor fails when the two kinds of activity are decoupled---a regime that is prevalent in real networks---thereby validating the necessity of dual independent correction.
Social and Information Networks (cs.SI); Machine Learning (cs.LG); Machine Learning (stat.ML)
arXiv:2610.40676 [cs.SI]
(or arXiv:2610.40676v1 [cs.SI] for this version)
https://doi.org/10.48550/arXiv.2610.40676 Focus to learn more arXiv-issued DOI via DataCite (pending registration)
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