Salīdzināt metodes

	Vērstā modularitātes analīze	Stohastiskais bloku modelis
Nozare	Tīklu analīze	Tīklu analīze
Saime	Machine learning	Process / pipeline
Izcelsmes gads	2008	1983
Autors	Leicht, E. A. & Newman, M. E. J.	—
Tips	Community detection / graph partitioning	Probabilistic generative graph model
Pirmavots	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.
Citi nosaukumi	directed community detection via modularity, directed Q-modularity, digraph modularity optimization, Leicht-Newman modularity	SBM, degree-corrected SBM, DCSBM, Stokastik Blok Modeli (SBM)
Saistītās	5	7
Kopsavilkums	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.