Methoden vergleichen

	Dynamische Zwei-Moden-Netzwerkanalyse	Dynamische Community-Erkennung
Fachgebiet	Netzwerkanalyse	Netzwerkanalyse
Familie	Machine learning	Machine learning
Entstehungsjahr	2000s–2012	2010 (key formalization); earlier work 2002–2009
Urheber	Borgatti, S. P. & Halgin, D. S. (affiliation networks); Holme, P. & Saramäki, J. (temporal networks)	Mucha, P. J. et al. (key formalization); earlier work by Girvan & Newman (2002)
Typ	Longitudinal bipartite network analysis	Graph clustering / community discovery
Wegweisende Quelle	Borgatti, S. P., & Halgin, D. S. (2011). Analyzing affiliation networks. In J. Scott & P. J. Carrington (Eds.), The SAGE Handbook of Social Network Analysis (pp. 417–433). SAGE.	Mucha, P. J., Richardson, T., Macon, K., Porter, M. A., & Onnela, J.-P. (2010). Community structure in time-dependent, multiscale, and multiplex networks. Science, 328(5980), 876–878.
Aliasnamen	Dynamic bipartite network analysis, Temporal two-mode network analysis, Longitudinal affiliation network analysis, Dynamic actor-event network analysis	DCD, temporal community detection, evolving community detection, dynamic graph clustering
Verwandt	6	5
Zusammenfassung	Dynamic two-mode network analysis studies bipartite networks — structures with two distinct node types, such as actors and events or authors and papers — as they evolve over time. By tracking how memberships, affiliations, and co-participations change across temporal snapshots, it reveals the emergence, dissolution, and reorganization of collaborative or membership structures that static analysis would miss.	Dynamic community detection identifies groups of densely connected nodes in networks that evolve over time, tracking how communities form, merge, split, and dissolve across temporal snapshots. Developed to extend static modularity optimization to time-varying structures, it is widely used in social, biological, and communication network research.