Vertaile menetelmiä

	Monikerroksinen sosiaalisten verkostojen analyysi	Yhteisöjen tunnistus
Tieteenala	Verkostoanalyysi	Verkostoanalyysi
Menetelmäperhe	Machine learning	Process / pipeline
Syntyvuosi	2014	2002–2019 (algorithm family)
Kehittäjä	Kivela, M.; Boccaletti, S. et al.	Louvain: Blondel et al. (2008); Leiden: Traag et al. (2019); Girvan-Newman: Girvan & Newman (2002); Infomap: Rosvall & Bergstrom (2008)
Tyyppi	Structural network analysis framework	Graph-partitioning / clustering algorithm family
Alkuperäislähde	Kivela, M., Arenas, A., Barthelemy, M., Gleeson, J. P., Moreno, Y., & Porter, M. A. (2014). Multilayer networks. Journal of Complex Networks, 2(3), 203–271.	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	MSNA, multiplex network analysis, multilayer network analysis, interconnected network analysis	graph clustering, network partitioning, Topluluk Tespiti (Louvain, Girvan-Newman, Leiden)
Liittyvät	6	5
Tiivistelmä	Multilayer social network analysis extends classical single-layer network methods to settings where actors are connected through multiple, distinct types of ties — such as friendship, professional collaboration, and online interaction — simultaneously. By modeling each type of relationship as a separate layer and explicitly representing connections across layers, it captures structural complexity that a single aggregated network would hide.	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?