Vertaile menetelmiä

	Pieni-maailma- ja skaalavapaa verkkotunnisteanalyysi	Yhteisöjen tunnistus
Tieteenala	Verkostoanalyysi	Verkostoanalyysi
Menetelmäperhe	Process / pipeline	Process / pipeline
Syntyvuosi	1998 (small-world); 1999 (scale-free)	2002–2019 (algorithm family)
Kehittäjä	—	Louvain: Blondel et al. (2008); Leiden: Traag et al. (2019); Girvan-Newman: Girvan & Newman (2002); Infomap: Rosvall & Bergstrom (2008)
Tyyppi	Descriptive / exploratory network analysis	Graph-partitioning / clustering algorithm family
Alkuperäislähde	Watts, D.J. & Strogatz, S.H. (1998). Collective Dynamics of 'Small-World' Networks. Nature, 393(6684), 440-442.	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.
Rinnakkaisnimet	Küçük Dünya ve Ölçek-Bağımsız Ağ Analizi, small-world network, scale-free network, preferential attachment analysis	graph clustering, network partitioning, Topluluk Tespiti (Louvain, Girvan-Newman, Leiden)
Liittyvät	9	5
Tiivistelmä	Small-world and scale-free network analysis tests whether a real-world network exhibits two landmark topological signatures identified in 1998-1999: the Watts-Strogatz small-world property (high local clustering combined with short average path lengths) and the Barabási-Albert scale-free property (a degree distribution that follows a power law, meaning a small number of hubs connect to a disproportionately large share of other nodes). Together these frameworks transformed network science by showing that many social, biological, and technological networks share a common structural grammar.	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?