Consistent community detection in multi-layer network data

Jing Lei, Kehui Chen and Brian Lynch

Biometrika, 2020, vol. 107, issue 1, 61-73

Abstract: SummaryWe consider multi-layer network data where the relationships between pairs of elements are reflected in multiple modalities, and may be described by multivariate or even high-dimensional vectors. Under the multi-layer stochastic block model framework we derive consistency results for a least squares estimation of memberships. Our theorems show that, as compared to single-layer community detection, a multi-layer network provides much richer information that allows for consistent community detection from a much sparser network, with required edge density reduced by a factor of the square root of the number of layers. Moreover, the multi-layer framework can detect cohesive community structure across layers, which might be hard to detect by any single-layer or simple aggregation. Simulations and a data example are provided to support the theoretical results.

Keywords: Community detection; Consistency; Sparse network; Tensor concentration bound (search for similar items in EconPapers)
Date: 2020
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Citations: View citations in EconPapers (3)

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