方法对比
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| 定向双模网络分析× | 有向社区检测× | |
|---|---|---|
| 领域 | 网络分析 | 网络分析 |
| 方法族 | Machine learning | Machine learning |
| 起源年份≠ | 1997 | 2008 |
| 提出者≠ | Borgatti, S. P. & Everett, M. G. | Leicht, E. A. & Newman, M. E. J.; Rosvall, M. & Bergstrom, C. T. |
| 类型≠ | Structural network analysis | Graph partitioning / modularity optimization |
| 开创性文献≠ | Wasserman, S. & Faust, K. (1994). Social Network Analysis: Methods and Applications (Ch. 8). Cambridge University Press. ISBN: 978-0-521-38707-1 | Leicht, E. A. & Newman, M. E. J. (2008). Community structure in directed networks. Physical Review Letters, 100(11), 118703. DOI ↗ |
| 别名 | directed bipartite network analysis, asymmetric affiliation network analysis, directed actor-event network analysis, directed two-mode graph analysis | directed graph clustering, community detection in digraphs, directed modularity optimization, directed network partitioning |
| 相关 | 6 | 6 |
| 摘要≠ | Directed two-mode network analysis studies bipartite graphs in which nodes belong to two distinct sets — such as actors and events, authors and papers, or firms and markets — and edges carry a direction, capturing asymmetric relationships like citation, referral, or endorsement. Combining the duality of two-mode structure with directed tie semantics reveals flow patterns and influence asymmetries that undirected or single-mode analyses would miss. | 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. |
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