手法を比較

	指数ランダムグラフモデル (ERGM / p*)	コミュニティ検出
分野	ネットワーク分析	ネットワーク分析
系統	Process / pipeline	Process / pipeline
提唱年	1986 (foundational); modern ERGM framework 1996–2007	2002–2019 (algorithm family)
提唱者	Frank & Strauss (1986); extended by Wasserman & Pattison (1996) and Robins et al. (2007)	Louvain: Blondel et al. (2008); Leiden: Traag et al. (2019); Girvan-Newman: Girvan & Newman (2002); Infomap: Rosvall & Bergstrom (2008)
種類	Probabilistic generative network model	Graph-partitioning / clustering algorithm family
原典	Robins, G., Pattison, P., Kalish, Y., & Lusher, D. (2007). An introduction to exponential random graph (p*) models for social networks. Social Networks, 29(2), 173-191.	Blondel, V.D., Guillaume, J.-L., Lambiotte, R. & Lefebvre, E. (2008). Fast Unfolding of Communities in Large Networks. Journal of Statistical Mechanics, 2008(10), P10008.
別名	ERGM, p-star model, p* model, Üstel Rastgele Graf Modeli (ERGM / p*)	graph clustering, network partitioning, Topluluk Tespiti (Louvain, Girvan-Newman, Leiden)
関連	6	5
概要	The Exponential Random Graph Model (ERGM), also known as the p* model, is a statistical framework for network analysis that models the probability of an observed network as a function of its local structural features — such as reciprocity, triangles, and degree distribution. Developed from the foundational work of Frank and Strauss (1986) and extended into the modern framework by Wasserman and Pattison (1996) and Robins et al. (2007), ERGM is the inferential standard for social network analysis, capable of testing whether observed network structures arise by chance or reflect genuine social processes.	Community detection is a family of graph-partitioning algorithms that discover densely connected sub-groups — communities — within a network. First formalised through the modularity measure by Girvan and Newman (2002), the field advanced rapidly with the Louvain method (Blondel et al., 2008), the Leiden refinement (Traag et al., 2019), and the information-theoretic Infomap approach. All variants answer the same question: which nodes cluster together more tightly among themselves than with the rest of the network?