So sánh phương pháp

	Phát hiện cộng đồng có trọng số	Phân tích tính mô-đun
Lĩnh vực	Phân tích mạng lưới	Phân tích mạng lưới
Họ	Machine learning	Machine learning
Năm ra đời	2004–2008	2004
Người khởi xướng	Newman, M. E. J.; Blondel et al.	Newman, M. E. J. & Girvan, M.
Loại	Graph clustering / community detection	Community detection / graph partitioning
Công trình gốc	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.	Newman, M. E. J., & Girvan, M. (2004). Finding and evaluating community structure in networks. Physical Review E, 69(2), 026113.
Tên gọi khác	weighted graph clustering, community detection on weighted networks, weighted modularity optimization, WCD	Q-modularity, community structure detection, network modularity optimization, graph partitioning by modularity
Liên quan	6	5
Tóm tắt	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.	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.