方法对比
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| 有向模块度分析× | 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 | 2008 | 2008 |
| 提出者≠ | Leicht, E. A. & Newman, M. E. J. | Leicht, E. A. & Newman, M. E. J.; Rosvall, M. & Bergstrom, C. T. |
| 类型≠ | Community detection / graph partitioning | Graph partitioning / modularity optimization |
| 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 ↗ | Leicht, E. A. & Newman, M. E. J. (2008). Community structure in directed networks. Physical Review Letters, 100(11), 118703. DOI ↗ |
| 别名 | directed community detection via modularity, directed Q-modularity, digraph modularity optimization, Leicht-Newman modularity | directed graph clustering, community detection in digraphs, directed modularity optimization, directed network partitioning |
| 相关≠ | 5 | 6 |
| 摘要≠ | Directed modularity analysis extends the classic Newman-Girvan modularity framework to directed graphs, where edges carry a source and a destination. Formalized by Leicht and Newman in 2008, it partitions nodes into communities by maximizing a modularity score that accounts for each node's separate in-degree and out-degree in the null model, making it the standard approach for community detection in citation networks, information flows, and other asymmetric relational data. | 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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