Linganisha mbinu

	Uchanganuzi wa Mtandao Wenye Njia Mbili Zinazobadilika	Utambuzi wa Jumuiya Zinazobadilika
Nyanja	Uchanganuzi wa Mitandao	Uchanganuzi wa Mitandao
Familia	Machine learning	Machine learning
Mwaka wa asili	2000s–2012	2010 (key formalization); earlier work 2002–2009
Mwanzilishi	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)
Aina	Longitudinal bipartite network analysis	Graph clustering / community discovery
Chanzo asilia	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.
Majina mbadala	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
Zinazohusiana	6	5
Muhtasari	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.