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| Phát hiện cộng đồng có hướng× | Độ trung tâm giữa các điểm theo hướng× | |
|---|---|---|
| Lĩnh vực | Phân tích mạng lưới | Phân tích mạng lưới |
| Họ | Machine learning | Machine learning |
| Năm ra đời≠ | 2008 | 1977 |
| Người khởi xướng≠ | Leicht, E. A. & Newman, M. E. J.; Rosvall, M. & Bergstrom, C. T. | Freeman, L. C. |
| Loại≠ | Graph partitioning / modularity optimization | Centrality measure (directed graph) |
| Công trình gốc≠ | 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 ↗ |
| Tên gọi khác | directed graph clustering, community detection in digraphs, directed modularity optimization, directed network partitioning | directed BC, digraph betweenness, asymmetric betweenness centrality, directed Freeman betweenness |
| Liên quan≠ | 6 | 5 |
| Tóm tắt≠ | 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. |
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