方法对比
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| 有向模块度分析× | 定向社交网络分析× | |
|---|---|---|
| 领域 | 网络分析 | 网络分析 |
| 方法族 | Machine learning | Machine learning |
| 起源年份≠ | 2008 | 1994 |
| 提出者≠ | Leicht, E. A. & Newman, M. E. J. | Wasserman, S. & Faust, K. |
| 类型≠ | Community detection / graph partitioning | Structural analysis of directed graphs |
| 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 ↗ | Wasserman, S. & Faust, K. (1994). Social Network Analysis: Methods and Applications. Cambridge University Press. ISBN: 978-0-521-38707-1 |
| 别名 | directed community detection via modularity, directed Q-modularity, digraph modularity optimization, Leicht-Newman modularity | directed SNA, digraph analysis, directed graph network analysis, asymmetric network analysis |
| 相关 | 5 | 5 |
| 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 Social Network Analysis (directed SNA) studies networks in which every tie has an explicit direction — from a sender to a receiver — rather than treating relationships as symmetric. It extends the classical SNA toolkit with in-degree, out-degree, reciprocity, and asymmetric path measures, making it the appropriate framework wherever relationship direction carries substantive meaning, such as citation flows, advice-seeking, follower graphs, or information cascades. |
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