Сравнение методов

	Анализ мультиплексных сетей	Обнаружение сообществ
Область	Сетевой анализ	Сетевой анализ
Семейство	Machine learning	Process / pipeline
Год появления	2014	2002–2019 (algorithm family)
Автор метода	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)
Тип	Structural network model	Graph-partitioning / clustering algorithm family
Основополагающий источник	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.
Другие названия	multiplex networks, multi-layer network analysis, multilayer network analysis, MNA	graph clustering, network partitioning, Topluluk Tespiti (Louvain, Girvan-Newman, Leiden)
Связанные	6	5
Сводка	Multiplex network analysis studies systems where the same set of nodes is connected by multiple distinct types of relationships, each represented as a separate network layer. By analyzing layers simultaneously rather than in isolation, it reveals how different relation types interact, reinforce each other, or compensate for one another across the same actors or entities.	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?