Community detection with nodal information: Likelihood and its variational approximation

Haolei Weng, Yang Feng

Research output: Contribution to journalArticlepeer-review


Community detection is one of the fundamental problems in the study of network data. Most existing community detection approaches only consider edge information as inputs, and the output could be suboptimal when nodal information is available. In such cases, it is desirable to leverage nodal information for the improvement of community detection accuracy. Towards this goal, we propose a flexible network model incorporating nodal information and develop likelihood-based inference methods. For the proposed methods, we establish favorable asymptotic properties as well as efficient algorithms for computation. Numerical experiments show the effectiveness of our methods in utilizing nodal information across a variety of simulated and real network data sets.

Original languageEnglish (US)
Article numbere428
Issue number1
StatePublished - Dec 2022


  • community detection
  • consistency
  • maximum likelihood
  • multilogistic regression
  • networks
  • profile likelihood
  • semidefinite programming
  • stochastic block model
  • variational inference

ASJC Scopus subject areas

  • Statistics and Probability
  • Statistics, Probability and Uncertainty


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