Salīdzināt metodes

	Svara Stohastiskais Bloku Modelis	Modulāritātes analīze
Nozare	Tīklu analīze	Tīklu analīze
Saime	Machine learning	Machine learning
Izcelsmes gads	2014	2004
Autors	Aicher, C.; Jacobs, A. Z.; Clauset, A.	Newman, M. E. J. & Girvan, M.
Tips	Generative probabilistic model	Community detection / graph partitioning
Pirmavots	Aicher, C., Jacobs, A. Z., & Clauset, A. (2014). Learning latent block structure in weighted networks. Journal of Complex Networks, 3(2), 221–248.	Newman, M. E. J., & Girvan, M. (2004). Finding and evaluating community structure in networks. Physical Review E, 69(2), 026113.
Citi nosaukumi	W-SBM, weighted SBM, weighted block model, weighted community detection via SBM	Q-modularity, community structure detection, network modularity optimization, graph partitioning by modularity
Saistītās	6	5
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.	Modularity analysis is a network science method, formalized by Newman and Girvan in 2004, that detects community structure in graphs by measuring whether edges are more concentrated within groups than expected by chance. Its scalar quality index Q guides algorithms that partition nodes into cohesive clusters, making it the most widely adopted framework for community detection in social, biological, and technological networks.