方法对比
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| 模块度分析× | 网络扩散分析× | |
|---|---|---|
| 领域 | 网络分析 | 网络分析 |
| 方法族 | Machine learning | Machine learning |
| 起源年份≠ | 2004 | 1927 (epidemic roots); network formalization 1990s–2000s |
| 提出者≠ | Newman, M. E. J. & Girvan, M. | Kermack, W. O. & McKendrick, A. G. |
| 类型≠ | Community detection / graph partitioning | Simulation / analytical model |
| 开创性文献≠ | Newman, M. E. J., & Girvan, M. (2004). Finding and evaluating community structure in networks. Physical Review E, 69(2), 026113. DOI ↗ | Kermack, W. O. & McKendrick, A. G. (1927). A contribution to the mathematical theory of epidemics. Proceedings of the Royal Society of London A, 115(772), 700–721. DOI ↗ |
| 别名 | Q-modularity, community structure detection, network modularity optimization, graph partitioning by modularity | diffusion on networks, information diffusion, contagion spreading model, network propagation model |
| 相关 | 5 | 5 |
| 摘要≠ | Modularity analysis is a network science method, formalized by Newman and Girvan in 2004, that detects community structure in graphs by measuring whether edges are more concentrated within groups than expected by chance. Its scalar quality index Q guides algorithms that partition nodes into cohesive clusters, making it the most widely adopted framework for community detection in social, biological, and technological networks. | Network diffusion analysis models how information, diseases, behaviors, or innovations spread across a graph of nodes and edges. Drawing on classical epidemic theory (SI, SIR, SIS) and modern network science, it tracks which nodes become infected, how quickly, and whether the spread reaches a global cascade or dies out locally. |
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