Bandingkan metode

	Deteksi Komunitas Dinamis	Analisis Modularitas
Bidang	Analisis Jaringan	Analisis Jaringan
Keluarga	Machine learning	Machine learning
Tahun asal	2010 (key formalization); earlier work 2002–2009	2004
Pencetus	Mucha, P. J. et al. (key formalization); earlier work by Girvan & Newman (2002)	Newman, M. E. J. & Girvan, M.
Tipe	Graph clustering / community discovery	Community detection / graph partitioning
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.	Newman, M. E. J., & Girvan, M. (2004). Finding and evaluating community structure in networks. Physical Review E, 69(2), 026113.
Alias	DCD, temporal community detection, evolving community detection, dynamic graph clustering	Q-modularity, community structure detection, network modularity optimization, graph partitioning by modularity
Terkait	5	5
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.	Modularity analysis is a network science method, formalized by Newman and Girvan in 2004, that detects community structure in graphs by measuring whether edges are more concentrated within groups than expected by chance. Its scalar quality index Q guides algorithms that partition nodes into cohesive clusters, making it the most widely adopted framework for community detection in social, biological, and technological networks.