Porovnat metody

	Analýza temporálních sítí	Detekce komunit	Sociální síťová analýza
Obor	Analýza sítí	Analýza sítí	Analýza sítí
Rodina	Process / pipeline	Process / pipeline	Machine learning
Rok vzniku	2012	2002–2019 (algorithm family)	1934 (sociometry); 1994 (modern formalization)
Tvůrce	Holme & Saramäki (2012) — seminal framework	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
Typ	Dynamic graph analysis	Graph-partitioning / clustering algorithm family	Structural/relational analysis framework
Původní zdroj	Holme, P. & Saramäki, J. (2012). Temporal Networks. Physics Reports, 519(3), 97-125.	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
Další názvy	dynamic network analysis, time-varying network analysis, Zamansal Ağ Analizi (Temporal / Dynamic Networks)	graph clustering, network partitioning, Topluluk Tespiti (Louvain, Girvan-Newman, Leiden)	SNA, network analysis, sociometric analysis, relational analysis
Příbuzné	3	5	5
Shrnutí	Temporal network analysis, formalised by Holme and Saramäki in their landmark 2012 Physics Reports survey, is the study of networks in which edges appear and disappear over time. Rather than collapsing all contacts into a single static graph, the approach preserves the precise timing of interactions — whether as contact sequences, time-stamped event lists, or windowed snapshots — and uses that timing to track how influence, disease, or information can actually propagate through the system.	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.