方法对比

	网络图锦分析	个人网络分析	社会网络分析
领域	网络分析	网络分析	网络分析
方法族	Process / pipeline	Process / pipeline	Machine learning
起源年份	2002	1992 (Burt); foundational measurement formalised by Marsden 2002	1934 (sociometry); 1994 (modern formalization)
提出者	—	Ronald S. Burt (structural holes framework); Peter V. Marsden (egocentric measures)	Moreno, J.L.; formalized by Wasserman & Faust
类型	Statistical pattern-detection method for directed graphs	Descriptive / relational network analysis	Structural/relational analysis framework
开创性文献	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.	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
别名	network motifs, subgraph significance profile, Ağ Motif Analizi (Network Motifs)	personal network analysis, egocentric network analysis, Ego Ağı Analizi (Personal Network Analysis)	SNA, network analysis, sociometric analysis, relational analysis
相关	3	6	5
摘要	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.	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.