Salīdzināt metodes

	Vērsta kopienu noteikšana	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.; Rosvall, M. & Bergstrom, C. T.	—
Tips	Graph partitioning / modularity optimization	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 graph clustering, community detection in digraphs, directed modularity optimization, directed network partitioning	SBM, degree-corrected SBM, DCSBM, Stokastik Blok Modeli (SBM)
Saistītās	6	7
Kopsavilkums	Directed community detection identifies densely interconnected groups of nodes in a directed network, accounting for the asymmetry of edges (e.g., A follows B does not imply B follows A). Adapting modularity or flow-based criteria to directed graphs reveals clusters that undirected methods systematically miss, making it essential for citation networks, follower graphs, and biological regulatory pathways.	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.