Community Detection With Contextual Multilayer Networks

• Published in: IEEE transactions on information theory Vol. 69; no. 5; pp. 3203 - 3239
• Main Authors: Ma, Zongming, Nandy, Sagnik
• Format: Journal Article
• Language: English
• Published: New York IEEE 01.05.2023; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: Algorithms; approximate message passing; Asymptotic methods; Asymptotic properties; Clustering; contextual SBM; Estimation; integrative data analysis; Mathematical models; Message passing; multilayer network; Multilayers; Mutual information; Nonhomogeneous media; phase transition; Phase transitions; Signal to noise ratio; Social networking (online); Soft sensors; stochastic block model
• DOI: 10.1109/TIT.2023.3238352
• ISSN: 0018-9448

In this paper, we study community detection when we observe <inline-formula> <tex-math notation="LaTeX">m </tex-math></inline-formula> sparse networks and a high dimensional covariate matrix, all encoding the same community structure among <inline-formula> <tex-math notation="LaTeX">n </tex-math></inline-formula> subjects. In the asymptotic regime where the number of features <inline-formula> <tex-math notation="LaTeX">p </tex-math></inline-formula> and the number of subjects <inline-formula> <tex-math notation="LaTeX">n </tex-math></inline-formula> grow proportionally, we derive an exact formula of asymptotic minimum mean square error (MMSE) for estimating the common community structure in the balanced two block case using an orchestrated approximate message passing algorithm. The formula implies the necessity of integrating information from multiple data sources. Consequently, it induces a sharp threshold of phase transition between the regime where detection (i.e., weak recovery) is possible and the regime where no procedure performs better than random guess. The asymptotic MMSE depends on the covariate signal-to-noise ratio in a more subtle way than the phase transition threshold. In the special case of <inline-formula> <tex-math notation="LaTeX">m=1 </tex-math></inline-formula>, our asymptotic MMSE formula complements the pioneering work Deshpande et al., (2018) which found the sharp threshold when <inline-formula> <tex-math notation="LaTeX">m=1 </tex-math></inline-formula>. A practical variant of the theoretically justified algorithm with spectral initialization leads to an estimator whose empirical MSEs closely approximate theoretical predictions over simulated examples.