مقایسهٔ روشها
روشهای انتخابی خود را کنار هم مرور کنید؛ ردیفهای متفاوت برجسته شدهاند.
| پیشبینی پیوند× | تحلیل تابآوری و آسیبپذیری شبکه× | |
|---|---|---|
| حوزه | تحلیل شبکه | تحلیل شبکه |
| خانواده | Process / pipeline | Process / pipeline |
| سال پیدایش≠ | 2003 | 2000 |
| پدیدآور≠ | — | Albert, Jeong & Barabási |
| نوع≠ | Network inference task | Network robustness / vulnerability framework |
| منبع بنیادین≠ | 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 ↗ |
| نامهای دیگر≠ | Bağlantı Tahmini (Link Prediction), missing link prediction, future link prediction, edge prediction | network vulnerability analysis, attack tolerance analysis, Ağ Dayanıklılığı ve Güvenlik Açığı Analizi |
| مرتبط | 5 | 5 |
| خلاصه≠ | 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. |
| ScholarGateمجموعهداده ↗ |
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