Consistent community detection in multi-relational data through restricted multi-layer stochastic blockmodel

Subhadeep Paul, Yuguo Chen

Research output: Contribution to journalArticlepeer-review


In recent years there has been an increased interest in statistical analysis of data with multiple types of relations among a set of entities. Such multi-relational data can be represented as multi-layer graphs where the set of vertices represents the entities and multiple types of edges represent the different relations among them. For community detection in multi-layer graphs, we consider two random graph models, the multi-layer stochastic blockmodel (MLSBM) and a model with a restricted parameter space, the restricted multi-layer stochastic blockmodel (RMLSBM). We derive consistency results for community assignments from the maximum likelihood estimators (MLEs) in both models where MLSBM is assumed to be the true model, and either the number of nodes or the number of types of edges or both grow. We compare MLEs in the two models with other baseline approaches, such as separate modeling of layers, aggregating the layers and majority voting. In simulations RMLSBM is shown to have advantage over MLSBM when either the growth rate of the number of communities is high or the growth rate of the average degree of the component graphs in the multi-graph is low. We also derive minimax rates of error and thresholds for achieving consistency of community detection in both models, which are then used to compare the multi-layer models with a baseline model, the aggregate stochastic block model. The simulation studies and real data applications confirm the superior performance of the multi-layer approaches in comparison to the baseline procedures.

Original languageEnglish (US)
Article numberA68
Pages (from-to)3807-3870
Number of pages64
JournalElectronic Journal of Statistics
Issue number2
StatePublished - 2016


  • Community detection
  • Consistency
  • Consistency thresholds
  • Minimax rates
  • Multi-layer networks
  • Stochastic blockmodel

ASJC Scopus subject areas

  • Statistics and Probability
  • Statistics, Probability and Uncertainty


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