Bandingkan metode

	Analisis Modularitas Dinamis	Analisis Jaringan Temporal
Bidang	Analisis Jaringan	Analisis Jaringan
Keluarga	Machine learning	Process / pipeline
Tahun asal	2010	2012
Pencetus	Mucha, P. J.; Porter, M. A.; and colleagues	Holme & Saramäki (2012) — seminal framework
Tipe	Community detection on temporal networks	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	dynamic community structure analysis, temporal modularity optimization, evolving community detection, time-varying modularity	dynamic network analysis, time-varying network analysis, Zamansal Ağ Analizi (Temporal / Dynamic Networks)
Terkait	5	3
Ringkasan	Dynamic modularity analysis extends the classical modularity framework to networks that evolve over time, detecting communities across a sequence of network snapshots while penalizing unnecessary community changes between time steps. It identifies cohesive groups and tracks how they form, merge, split, or dissolve, giving researchers a principled view of structural change in longitudinal network data.	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.