השוואת שיטות

	מודל הבלוקים הסטוכסטי המשוקלל	ניתוח רשתות חברתיות משוקללות
תחום	ניתוח רשתות	ניתוח רשתות
משפחה	Machine learning	Machine learning
שנת המקור	2014	2004–2010
הוגה השיטה	Aicher, C.; Jacobs, A. Z.; Clauset, A.	Barrat, A.; Opsahl, T. et al.
סוג	Generative probabilistic model	Network analysis framework
מקור מכונן	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.
כינויים	W-SBM, weighted SBM, weighted block model, weighted community detection via SBM	Weighted SNA, valued network analysis, tie-strength network analysis, weighted graph analysis
קשורות	6	6
תקציר	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.