방법 비교

	가중치 확률 블록 모델	가중치 사회 연결망 분석 (Weighted Social Network Analysis)
분야	네트워크 분석	네트워크 분석
계열	Machine learning	Machine learning
기원 연도	2014	2004–2010
창시자	Aicher, C.; Jacobs, A. Z.; Clauset, A.	Barrat, A.; Opsahl, T. et al.
유형	Generative probabilistic model	Network analysis framework
원전	Aicher, C., Jacobs, A. Z., & Clauset, A. (2014). Learning latent block structure in weighted networks. Journal of Complex Networks, 3(2), 221–248.	Barrat, A., Barthélemy, M., Pastor-Satorras, R., & Vespignani, A. (2004). The architecture of complex weighted networks. Proceedings of the National Academy of Sciences, 101(11), 3747–3752.
별칭	W-SBM, weighted SBM, weighted block model, weighted community detection via SBM	Weighted SNA, valued network analysis, tie-strength network analysis, weighted graph analysis
관련	6	6
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.	Weighted Social Network Analysis extends classical SNA by assigning numeric values — weights — to ties between actors, capturing tie strength, interaction frequency, or resource flow. Rather than treating all connections as equal, it reveals who holds privileged positions by virtue of the intensity, not merely the existence, of their relationships.