Salīdzināt metodes

	Dinamiskā divu režīmu tīklu analīze	Laika tīklu analīze
Nozare	Tīklu analīze	Tīklu analīze
Saime	Machine learning	Process / pipeline
Izcelsmes gads	2000s–2012	2012
Autors	Borgatti, S. P. & Halgin, D. S. (affiliation networks); Holme, P. & Saramäki, J. (temporal networks)	Holme & Saramäki (2012) — seminal framework
Tips	Longitudinal bipartite network analysis	Dynamic graph analysis
Pirmavots	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.	Holme, P. & Saramäki, J. (2012). Temporal Networks. Physics Reports, 519(3), 97-125.
Citi nosaukumi	Dynamic bipartite network analysis, Temporal two-mode network analysis, Longitudinal affiliation network analysis, Dynamic actor-event network analysis	dynamic network analysis, time-varying network analysis, Zamansal Ağ Analizi (Temporal / Dynamic Networks)
Saistītās	6	3
Kopsavilkums	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.	Temporal network analysis, formalised by Holme and Saramäki in their landmark 2012 Physics Reports survey, is the study of networks in which edges appear and disappear over time. Rather than collapsing all contacts into a single static graph, the approach preserves the precise timing of interactions — whether as contact sequences, time-stamped event lists, or windowed snapshots — and uses that timing to track how influence, disease, or information can actually propagate through the system.