方法对比
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| 模块度分析× | 中间性中心度× | |
|---|---|---|
| 领域 | 网络分析 | 网络分析 |
| 方法族 | Machine learning | Machine learning |
| 起源年份≠ | 2004 | 1977 |
| 提出者≠ | Newman, M. E. J. & Girvan, M. | Freeman, L. C. |
| 类型≠ | Community detection / graph partitioning | Centrality measure |
| 开创性文献≠ | Newman, M. E. J., & Girvan, M. (2004). Finding and evaluating community structure in networks. Physical Review E, 69(2), 026113. DOI ↗ | Freeman, L. C. (1977). A set of measures of centrality based on betweenness. Sociometry, 40(1), 35–41. DOI ↗ |
| 别名 | Q-modularity, community structure detection, network modularity optimization, graph partitioning by modularity | Freeman betweenness, BC, geodesic betweenness, shortest-path betweenness |
| 相关≠ | 5 | 6 |
| 摘要≠ | 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. | 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. |
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