方法对比
并排查看您选择的方法；存在差异的行会高亮显示。
| 有向社区检测× | 有向介数中心性× | |
|---|---|---|
| 领域 | 网络分析 | 网络分析 |
| 方法族 | Machine learning | Machine learning |
| 起源年份≠ | 2008 | 1977 |
| 提出者≠ | Leicht, E. A. & Newman, M. E. J.; Rosvall, M. & Bergstrom, C. T. | Freeman, L. C. |
| 类型≠ | Graph partitioning / modularity optimization | Centrality measure (directed graph) |
| 开创性文献≠ | Leicht, E. A. & Newman, M. E. J. (2008). Community structure in directed networks. Physical Review Letters, 100(11), 118703. DOI ↗ | Freeman, L. C. (1977). A set of measures of centrality based on betweenness. Sociometry, 40(1), 35–41. DOI ↗ |
| 别名 | directed graph clustering, community detection in digraphs, directed modularity optimization, directed network partitioning | directed BC, digraph betweenness, asymmetric betweenness centrality, directed Freeman betweenness |
| 相关≠ | 6 | 5 |
| 摘要≠ | Directed community detection identifies densely interconnected groups of nodes in a directed network, accounting for the asymmetry of edges (e.g., A follows B does not imply B follows A). Adapting modularity or flow-based criteria to directed graphs reveals clusters that undirected methods systematically miss, making it essential for citation networks, follower graphs, and biological regulatory pathways. | Directed Betweenness Centrality extends Freeman's classic betweenness measure to directed graphs, quantifying how often a node lies on the shortest directed paths between all other pairs of nodes. It identifies gatekeepers, brokers, and bottlenecks in asymmetric flows such as information cascades, citation networks, and organizational hierarchies. |
| ScholarGate数据集 ↗ |
|
|