So sánh phương pháp

	Phát hiện cộng đồng theo thời gian	Phát hiện cộng đồng có trọng số
Lĩnh vực	Phân tích mạng lưới	Phân tích mạng lưới
Họ	Machine learning	Machine learning
Năm ra đời	2010	2004–2008
Người khởi xướng	Mucha, P. J. et al.	Newman, M. E. J.; Blondel et al.
Loại	Network clustering algorithm	Graph clustering / community detection
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.	Blondel, V. D., Guillaume, J.-L., Lambiotte, R., & Lefebvre, E. (2008). Fast unfolding of communities in large networks. Journal of Statistical Mechanics: Theory and Experiment, 2008(10), P10008.
Tên gọi khác	dynamic community detection, time-varying community detection, evolutionary community detection, longitudinal community detection	weighted graph clustering, community detection on weighted networks, weighted modularity optimization, WCD
Liên quan	6	6
Tóm tắt	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.	Weighted community detection identifies densely connected groups — communities — in networks where edges carry numeric strengths (weights). By incorporating edge weights into the modularity function, it reveals structure that binary adjacency alone would miss: two nodes connected by a strong tie are treated as more similar than two nodes linked by a weak one. The Louvain algorithm is the dominant practical implementation.