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	ネットワークモティーフ解析	コミュニティ検出	エゴネットワーク分析	ソーシャルネットワーク分析
分野	ネットワーク分析	ネットワーク分析	ネットワーク分析	ネットワーク分析
系統	Process / pipeline	Process / pipeline	Process / pipeline	Machine learning
提唱年	2002	2002–2019 (algorithm family)	1992 (Burt); foundational measurement formalised by Marsden 2002	1934 (sociometry); 1994 (modern formalization)
提唱者	—	Louvain: Blondel et al. (2008); Leiden: Traag et al. (2019); Girvan-Newman: Girvan & Newman (2002); Infomap: Rosvall & Bergstrom (2008)	Ronald S. Burt (structural holes framework); Peter V. Marsden (egocentric measures)	Moreno, J.L.; formalized by Wasserman & Faust
種類	Statistical pattern-detection method for directed graphs	Graph-partitioning / clustering algorithm family	Descriptive / relational network analysis	Structural/relational analysis framework
原典	Milo, R., Shen-Orr, S., Itzkovitz, S., Kashtan, N., Chklovskii, D., & Alon, U. (2002). Network Motifs: Simple Building Blocks of Complex Networks. Science, 298(5594), 824-827.	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.	Burt, R.S. (1992). Structural Holes: The Social Structure of Competition. Harvard University Press. ISBN: 9780674843714	Wasserman, S. & Faust, K. (1994). Social Network Analysis: Methods and Applications. Cambridge University Press. ISBN: 978-0-521-38707-1
別名	network motifs, subgraph significance profile, Ağ Motif Analizi (Network Motifs)	graph clustering, network partitioning, Topluluk Tespiti (Louvain, Girvan-Newman, Leiden)	personal network analysis, egocentric network analysis, Ego Ağı Analizi (Personal Network Analysis)	SNA, network analysis, sociometric analysis, relational analysis
関連	3	5	6	5
概要	Network motif analysis is a statistical method for directed networks, introduced by Milo, Shen-Orr, and Alon in 2002, that identifies small recurring subgraph patterns — motifs — that appear significantly more often than would be expected in a comparable random network. By comparing a real network against a null ensemble of randomised graphs, the method reveals the elementary structural building blocks that define the functional organisation of biological regulatory networks, social networks, and other complex systems.	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?	Ego network analysis examines the personal network of a focal individual — the ego — by mapping their direct contacts (alters) and the ties those contacts share with one another. Formalised through Ronald Burt's structural holes framework (1992) and Marsden's egocentric measurement approach (2002), the method produces ego-level indicators such as network size, density, constraint, and brokerage role that reveal how each individual's social position shapes their access to information, resources, and influence.	Social Network Analysis (SNA) is a structural method that maps and measures relationships and flows between people, groups, organizations, or other entities modeled as nodes connected by ties (edges). Rather than focusing on individual attributes, SNA reveals how the pattern of connections shapes behavior, influence, information flow, and outcomes within a system.