Methoden vergleichen

	Gewichtete Multiplex-Netzwerkanalyse	Gewichtete Gemeinschaftserkennung
Fachgebiet	Netzwerkanalyse	Netzwerkanalyse
Familie	Machine learning	Machine learning
Entstehungsjahr	2014	2004–2008
Urheber	Battiston, F.; Kivela, M. et al.	Newman, M. E. J.; Blondel et al.
Typ	Network analysis framework	Graph clustering / community detection
Wegweisende Quelle	Battiston, F., Nicosia, V., & Latora, V. (2014). Structural measures for multiplex networks. Physical Review E, 89(3), 032804.	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.
Aliasnamen	WMNA, weighted multilayer network analysis, weighted multi-relational network analysis, multiplex weighted graph analysis	weighted graph clustering, community detection on weighted networks, weighted modularity optimization, WCD
Verwandt	5	6
Zusammenfassung	Weighted multiplex network analysis studies systems in which the same set of actors are connected through multiple types of relationships simultaneously, and each relationship carries a quantitative strength or frequency. By capturing both the variety and the intensity of ties across layers, it reveals patterns invisible to single-layer or unweighted network approaches.	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.