方法证据记录
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.
源记录
引文逐字复制自方法源记录。这些引文不代表任何层级的验证。
Weighted Stochastic Block Model (W-SBM)
分类方法记录 · ml-model / network-analysis
- 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
- 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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