Bandingkan metode

	Model Blok Stokastik Tertimbang	Model Graf Acak Berbobot Eksponensial
Bidang	Analisis Jaringan	Analisis Jaringan
Keluarga	Machine learning	Machine learning
Tahun asal	2014	2012
Pencetus	Aicher, C.; Jacobs, A. Z.; Clauset, A.	Krivitsky, P. N.
Tipe	Generative probabilistic model	Statistical network model
Sumber perintis	Aicher, C., Jacobs, A. Z., & Clauset, A. (2014). Learning latent block structure in weighted networks. Journal of Complex Networks, 3(2), 221–248.	Krivitsky, P. N. (2012). Exponential-family random graph models for valued networks. Electronic Journal of Statistics, 6, 1100–1128.
Alias	W-SBM, weighted SBM, weighted block model, weighted community detection via SBM	W-ERGM, valued ERGM, weighted p-star model, valued exponential random graph model
Terkait	6	4
Ringkasan	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.	The Weighted Exponential Random Graph Model (W-ERGM) extends the classic binary ERGM framework to networks whose edges carry quantitative values — such as frequency of contact, trade volume, or collaboration intensity. It models the entire valued-edge network as a probability distribution defined over all possible weighted graphs, enabling researchers to test whether structural patterns such as reciprocity, transitivity, or degree distribution arise beyond what chance alone would produce.