So sánh phương pháp

	Phát hiện cộng đồng động	Phát hiện cộng đồng theo thời gian
Lĩnh vực	Phân tích mạng lưới	Phân tích mạng lưới
Họ	Machine learning	Machine learning
Năm ra đời	2010 (key formalization); earlier work 2002–2009	2010
Người khởi xướng	Mucha, P. J. et al. (key formalization); earlier work by Girvan & Newman (2002)	Mucha, P. J. et al.
Loại	Graph clustering / community discovery	Network clustering algorithm
Công trình gốc	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.	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.
Tên gọi khác	DCD, temporal community detection, evolving community detection, dynamic graph clustering	dynamic community detection, time-varying community detection, evolutionary community detection, longitudinal community detection
Liên quan	5	6
Tóm tắt	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 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.