方法对比
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| 链接预测× | 网络韧性与脆弱性分析× | |
|---|---|---|
| 领域 | 网络分析 | 网络分析 |
| 方法族 | Process / pipeline | Process / pipeline |
| 起源年份≠ | 2003 | 2000 |
| 提出者≠ | — | Albert, Jeong & Barabási |
| 类型≠ | Network inference task | Network robustness / vulnerability framework |
| 开创性文献≠ | Liben-Nowell, D. & Kleinberg, J. (2007). The Link-Prediction Problem for Social Networks. Journal of the American Society for Information Science and Technology, 58(7), 1019-1031. DOI ↗ | Albert, R., Jeong, H. & Barabási, A.L. (2000). Error and attack tolerance of complex networks. Nature, 406, 378–382. DOI ↗ |
| 别名≠ | Bağlantı Tahmini (Link Prediction), missing link prediction, future link prediction, edge prediction | network vulnerability analysis, attack tolerance analysis, Ağ Dayanıklılığı ve Güvenlik Açığı Analizi |
| 相关 | 5 | 5 |
| 摘要≠ | Link prediction is a network-analysis task that estimates which edges are missing from an observed graph or which edges are likely to form in the future. Formalised by Liben-Nowell and Kleinberg (2003, 2007), it covers a spectrum of approaches — from simple structural similarity indices such as Common Neighbors, Jaccard coefficient, and Adamic-Adar, to matrix factorisation, and graph neural network (GNN) methods — and is evaluated with AUC and Average Precision to account for the heavily imbalanced ratio of real to non-existing edges. | Network resilience and vulnerability analysis is an analytical framework, formalised by Albert, Jeong, and Barabási (2000), that measures how a network degrades functionally as nodes or edges are progressively removed. By running targeted-attack simulations — removing the highest-centrality nodes first — and random-failure simulations — removing nodes at uniform probability — the framework identifies which structural elements are critical to network integrity and where infrastructure is most exposed. |
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