Sammenlign metoder

	Rettet modularitetsanalyse	Stochastic Block Model
Fagfelt	Nettverksanalyse	Nettverksanalyse
Familie	Machine learning	Process / pipeline
Opprinnelsesår	2008	1983
Opphavsperson	Leicht, E. A. & Newman, M. E. J.	—
Type	Community detection / graph partitioning	Probabilistic generative graph model
Opprinnelig kilde	Leicht, E. A., & Newman, M. E. J. (2008). Community structure in directed networks. Physical Review Letters, 100(11), 118703.	Holland, P.W., Laskey, K.B. & Leinhardt, S. (1983). Stochastic Blockmodels: First Steps. Social Networks, 5(2), 109-137.
Alias	directed community detection via modularity, directed Q-modularity, digraph modularity optimization, Leicht-Newman modularity	SBM, degree-corrected SBM, DCSBM, Stokastik Blok Modeli (SBM)
Relaterte	5	7
Sammendrag	Directed modularity analysis extends the classic Newman-Girvan modularity framework to directed graphs, where edges carry a source and a destination. Formalized by Leicht and Newman in 2008, it partitions nodes into communities by maximizing a modularity score that accounts for each node's separate in-degree and out-degree in the null model, making it the standard approach for community detection in citation networks, information flows, and other asymmetric relational data.	The Stochastic Block Model (SBM), introduced by Holland, Laskey and Leinhardt (1983), is a probabilistic generative model for graphs that assigns nodes to latent blocks and parametrically estimates the connection probabilities between blocks. It is the foundational approach for community detection, core-periphery identification, and hierarchical structure discovery in network analysis.