手法を比較

	有向コミュニティ検出	確率的ブロックモデル
分野	ネットワーク分析	ネットワーク分析
系統	Machine learning	Process / pipeline
提唱年	2008	1983
提唱者	Leicht, E. A. & Newman, M. E. J.; Rosvall, M. & Bergstrom, C. T.	—
種類	Graph partitioning / modularity optimization	Probabilistic generative graph model
原典	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.
別名	directed graph clustering, community detection in digraphs, directed modularity optimization, directed network partitioning	SBM, degree-corrected SBM, DCSBM, Stokastik Blok Modeli (SBM)
関連	6	7
概要	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.