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| Phân tích mạng hai chế độ 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≠ | 1997 (two-mode); weighted extensions 2000s | 2004 |
| Người khởi xướng≠ | Borgatti, S. P. & Everett, M. G. | Newman, M. E. J. & Girvan, M. |
| Loại≠ | Network structural analysis | Community detection / graph partitioning |
| Công trình gốc≠ | Borgatti, S. P., & Everett, M. G. (1997). Network analysis of 2-mode data. Social Networks, 19(3), 243–269. DOI ↗ | Newman, M. E. J., & Girvan, M. (2004). Finding and evaluating community structure in networks. Physical Review E, 69(2), 026113. DOI ↗ |
| Tên gọi khác | weighted bipartite network analysis, valued two-mode network analysis, weighted affiliation network analysis, W2MNA | Q-modularity, community structure detection, network modularity optimization, graph partitioning by modularity |
| Liên quan≠ | 6 | 5 |
| Tóm tắt≠ | Weighted two-mode network analysis examines bipartite graphs in which two distinct node sets — such as actors and events, authors and papers, or species and habitats — are connected by edges carrying numerical weights that capture the strength, frequency, or intensity of each affiliation. Incorporating weights provides substantially richer structural insights than unweighted bipartite analysis. | 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. |
| ScholarGateBộ dữ liệu ↗ |
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