השוואת שיטות

	זיהוי קהילות	ניתוח רשתות חברתיות
תחום	ניתוח רשתות	ניתוח רשתות
משפחה	Process / pipeline	Machine learning
שנת המקור	2002–2019 (algorithm family)	1934 (sociometry); 1994 (modern formalization)
הוגה השיטה	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
סוג	Graph-partitioning / clustering algorithm family	Structural/relational analysis framework
מקור מכונן	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
כינויים	graph clustering, network partitioning, Topluluk Tespiti (Louvain, Girvan-Newman, Leiden)	SNA, network analysis, sociometric analysis, relational analysis
קשורות	5	5
תקציר	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.