So sánh phương pháp

	Phân tích Mô-típ Mạng	Phát hiện Cộng đồng	Phân tích Mạng Lưới Cá Nhân
Lĩnh vực	Phân tích mạng lưới	Phân tích mạng lưới	Phân tích mạng lưới
Họ	Process / pipeline	Process / pipeline	Process / pipeline
Năm ra đời	2002	2002–2019 (algorithm family)	1992 (Burt); foundational measurement formalised by Marsden 2002
Người khởi xướng	—	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)
Loại	Statistical pattern-detection method for directed graphs	Graph-partitioning / clustering algorithm family	Descriptive / relational network analysis
Công trình gốc	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
Tên gọi khác	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)
Liên quan	3	5	6
Tóm tắt	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.