Salīdzināt metodes

	Tīkla motīvu analīze	Kopienu noteikšana	Sociālo tīklu analīze
Nozare	Tīklu analīze	Tīklu analīze	Tīklu analīze
Saime	Process / pipeline	Process / pipeline	Machine learning
Izcelsmes gads	2002	2002–2019 (algorithm family)	1934 (sociometry); 1994 (modern formalization)
Autors	—	Louvain: Blondel et al. (2008); Leiden: Traag et al. (2019); Girvan-Newman: Girvan & Newman (2002); Infomap: Rosvall & Bergstrom (2008)	Moreno, J.L.; formalized by Wasserman & Faust
Tips	Statistical pattern-detection method for directed graphs	Graph-partitioning / clustering algorithm family	Structural/relational analysis framework
Pirmavots	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.	Wasserman, S. & Faust, K. (1994). Social Network Analysis: Methods and Applications. Cambridge University Press. ISBN: 978-0-521-38707-1
Citi nosaukumi	network motifs, subgraph significance profile, Ağ Motif Analizi (Network Motifs)	graph clustering, network partitioning, Topluluk Tespiti (Louvain, Girvan-Newman, Leiden)	SNA, network analysis, sociometric analysis, relational analysis
Saistītās	3	5	5
Kopsavilkums	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?	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.