방법 비교

	가중치 확률 블록 모델	모듈성 분석
분야	네트워크 분석	네트워크 분석
계열	Machine learning	Machine learning
기원 연도	2014	2004
창시자	Aicher, C.; Jacobs, A. Z.; Clauset, A.	Newman, M. E. J. & Girvan, M.
유형	Generative probabilistic model	Community detection / graph partitioning
원전	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.
별칭	W-SBM, weighted SBM, weighted block model, weighted community detection via SBM	Q-modularity, community structure detection, network modularity optimization, graph partitioning by modularity
관련	6	5
요약	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.