So sánh phương pháp
Xem các phương pháp đã chọn cạnh nhau; những hàng khác biệt được làm nổi bật.
| Phân tích Modularity Có trọng số× | Độ trung tâm giữa× | |
|---|---|---|
| Lĩnh vực | Phân tích mạng lưới | Phân tích mạng lưới |
| Họ | Machine learning | Machine learning |
| Năm ra đời≠ | 2004 | 1977 |
| Người khởi xướng≠ | Newman, M. E. J. | Freeman, L. C. |
| Loại≠ | Community structure optimization on weighted graphs | Centrality measure |
| Công trình gốc≠ | Newman, M. E. J. (2004). Analysis of weighted networks. Physical Review E, 70(5), 056131. DOI ↗ | Freeman, L. C. (1977). A set of measures of centrality based on betweenness. Sociometry, 40(1), 35–41. DOI ↗ |
| Tên gọi khác | weighted modularity, weighted Q optimization, weighted network community detection, strength-based modularity | Freeman betweenness, BC, geodesic betweenness, shortest-path betweenness |
| Liên quan≠ | 5 | 6 |
| Tóm tắt≠ | Weighted modularity analysis extends the classical Newman-Girvan modularity measure to networks where edges carry numeric strengths (frequencies, intensities, costs). By replacing binary adjacency with tie weights, it finds community partitions that reflect how densely interconnected subgroups are relative to what is expected under a weighted null model, yielding more nuanced groupings than unweighted approaches on data where edge strength varies meaningfully. | Betweenness centrality, formalized by Linton C. Freeman in 1977, measures how often a node lies on the shortest path connecting every other pair of nodes in a network. High-betweenness nodes act as bridges or brokers: removing them fragments the network into disconnected components more severely than removing any other nodes. |
| ScholarGateBộ dữ liệu ↗ |
|
|