Salīdzināt metodes

	Svara Stohastiskais Bloku Modelis	Svērtais eksponenciālais nejaušo grafu modelis (W-ERGM)
Nozare	Tīklu analīze	Tīklu analīze
Saime	Machine learning	Machine learning
Izcelsmes gads	2014	2012
Autors	Aicher, C.; Jacobs, A. Z.; Clauset, A.	Krivitsky, P. N.
Tips	Generative probabilistic model	Statistical network model
Pirmavots	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.
Citi nosaukumi	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
Saistītās	6	4
Kopsavilkums	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.