Machine learningNetwork science

Weighted Stochastic Block Model

The Weighted Stochastic Block Model (W-SBM) extends the classical stochastic block model to networks whose edges carry numerical weights. By positing that edge weights between node pairs arise from distributions that depend on the block memberships of those nodes, it simultaneously infers a partition of nodes into communities and a set of block-to-block weight parameters — recovering structure invisible to unweighted methods.

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Sources

  1. Aicher, C., Jacobs, A. Z., & Clauset, A. (2014). Learning latent block structure in weighted networks. Journal of Complex Networks, 3(2), 221–248. DOI: 10.1093/comnet/cnu026
  2. Nowicki, K., & Snijders, T. A. B. (2001). Estimation and prediction for stochastic blockstructures. Journal of the American Statistical Association, 96(455), 1077–1087. DOI: 10.1198/016214501753208735

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Referenced by

ScholarGateWeighted Stochastic Block Model (Weighted Stochastic Block Model (W-SBM)). Retrieved 2026-06-04 from https://scholargate.app/tr/network-analysis/weighted-stochastic-block-model