方法对比
并排查看您选择的方法；存在差异的行会高亮显示。
| 动态模块性分析× | 模块度分析× | |
|---|---|---|
| 领域 | 网络分析 | 网络分析 |
| 方法族 | Machine learning | Machine learning |
| 起源年份≠ | 2010 | 2004 |
| 提出者≠ | Mucha, P. J.; Porter, M. A.; and colleagues | Newman, M. E. J. & Girvan, M. |
| 类型≠ | Community detection on temporal networks | Community detection / graph partitioning |
| 开创性文献≠ | Mucha, P. J., Richardson, T., Macon, K., Porter, M. A., & Onnela, J.-P. (2010). Community structure in time-dependent, multiscale, and multiplex networks. Science, 328(5980), 876–878. DOI ↗ | Newman, M. E. J., & Girvan, M. (2004). Finding and evaluating community structure in networks. Physical Review E, 69(2), 026113. DOI ↗ |
| 别名 | dynamic community structure analysis, temporal modularity optimization, evolving community detection, time-varying modularity | Q-modularity, community structure detection, network modularity optimization, graph partitioning by modularity |
| 相关 | 5 | 5 |
| 摘要≠ | Dynamic modularity analysis extends the classical modularity framework to networks that evolve over time, detecting communities across a sequence of network snapshots while penalizing unnecessary community changes between time steps. It identifies cohesive groups and tracks how they form, merge, split, or dissolve, giving researchers a principled view of structural change in longitudinal network data. | 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. |
| ScholarGate数据集 ↗ |
|
|