Σύγκριση μεθόδων

	Ανίχνευση Κοινοτήτων με Βαρύτητα	Ανάλυση Κοινωνικών Δικτύων
Πεδίο	Ανάλυση Δικτύων	Ανάλυση Δικτύων
Οικογένεια	Machine learning	Machine learning
Έτος προέλευσης	2004–2008	1934 (sociometry); 1994 (modern formalization)
Δημιουργός	Newman, M. E. J.; Blondel et al.	Moreno, J.L.; formalized by Wasserman & Faust
Τύπος	Graph clustering / community detection	Structural/relational analysis framework
Θεμελιώδης πηγή	Blondel, V. D., Guillaume, J.-L., Lambiotte, R., & Lefebvre, E. (2008). Fast unfolding of communities in large networks. Journal of Statistical Mechanics: Theory and Experiment, 2008(10), P10008.	Wasserman, S. & Faust, K. (1994). Social Network Analysis: Methods and Applications. Cambridge University Press. ISBN: 978-0-521-38707-1
Εναλλακτικές ονομασίες	weighted graph clustering, community detection on weighted networks, weighted modularity optimization, WCD	SNA, network analysis, sociometric analysis, relational analysis
Συναφείς	6	5
Σύνοψη	Weighted community detection identifies densely connected groups — communities — in networks where edges carry numeric strengths (weights). By incorporating edge weights into the modularity function, it reveals structure that binary adjacency alone would miss: two nodes connected by a strong tie are treated as more similar than two nodes linked by a weak one. The Louvain algorithm is the dominant practical implementation.	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.