手法を比較

	時間的コミュニティ検出	時間的ネットワーク分析
分野	ネットワーク分析	ネットワーク分析
系統	Machine learning	Process / pipeline
提唱年	2010	2012
提唱者	Mucha, P. J. et al.	Holme & Saramäki (2012) — seminal framework
種類	Network clustering algorithm	Dynamic graph analysis
原典	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.	Holme, P. & Saramäki, J. (2012). Temporal Networks. Physics Reports, 519(3), 97-125.
別名	dynamic community detection, time-varying community detection, evolutionary community detection, longitudinal community detection	dynamic network analysis, time-varying network analysis, Zamansal Ağ Analizi (Temporal / Dynamic Networks)
関連	6	3
概要	Temporal community detection identifies cohesive groups (communities) in networks whose structure changes over time. By treating each time snapshot as a network layer and coupling consecutive layers, it reveals how communities form, merge, split, grow, or dissolve — turning a sequence of static snapshots into a continuous narrative of group evolution.	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.