方法对比
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| 加权时间网络分析× | 时间网络分析× | |
|---|---|---|
| 领域 | 网络分析 | 网络分析 |
| 方法族≠ | Machine learning | Process / pipeline |
| 起源年份≠ | 2004–2012 | 2012 |
| 提出者≠ | Holme, P. & Saramaki, J. (temporal networks); Barrat et al. (weighted networks) | Holme & Saramäki (2012) — seminal framework |
| 类型≠ | Network analysis technique | Dynamic graph analysis |
| 开创性文献≠ | Holme, P. & Saramaki, J. (2012). Temporal networks. Physics Reports, 519(3), 97–125. DOI ↗ | Holme, P. & Saramäki, J. (2012). Temporal Networks. Physics Reports, 519(3), 97-125. DOI ↗ |
| 别名≠ | WTNA, weighted time-varying network analysis, weighted dynamic network analysis, weighted evolving network analysis | dynamic network analysis, time-varying network analysis, Zamansal Ağ Analizi (Temporal / Dynamic Networks) |
| 相关≠ | 6 | 3 |
| 摘要≠ | Weighted temporal network analysis studies networks whose edges carry numerical weights — representing interaction strength, frequency, or intensity — and whose structure changes over time. It combines the time-varying perspective of temporal network analysis with the quantitative precision of weighted graph metrics, revealing not only when connections exist but how strong they are at each moment. | Temporal network analysis, formalised by Holme and Saramäki in their landmark 2012 Physics Reports survey, is the study of networks in which edges appear and disappear over time. Rather than collapsing all contacts into a single static graph, the approach preserves the precise timing of interactions — whether as contact sequences, time-stamped event lists, or windowed snapshots — and uses that timing to track how influence, disease, or information can actually propagate through the system. |
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