So sánh phương pháp

	Phân tích Modularity 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	2010
Người khởi xướng	Mucha, P. J.; Porter, M. A.; and colleagues	Mucha, P. J. et al.
Loại	Community detection on temporal networks	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	dynamic community structure analysis, temporal modularity optimization, evolving community detection, time-varying modularity	dynamic community detection, time-varying community detection, evolutionary community detection, longitudinal community detection
Liên quan	5	6
Tóm tắt	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 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.