Bandingkan metode

	Deteksi Komunitas Dinamis	Analisis Jaringan Temporal
Bidang	Analisis Jaringan	Analisis Jaringan
Keluarga	Machine learning	Process / pipeline
Tahun asal	2010 (key formalization); earlier work 2002–2009	2012
Pencetus	Mucha, P. J. et al. (key formalization); earlier work by Girvan & Newman (2002)	Holme & Saramäki (2012) — seminal framework
Tipe	Graph clustering / community discovery	Dynamic graph analysis
Sumber perintis	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.
Alias	DCD, temporal community detection, evolving community detection, dynamic graph clustering	dynamic network analysis, time-varying network analysis, Zamansal Ağ Analizi (Temporal / Dynamic Networks)
Terkait	5	3
Ringkasan	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.	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.