方法对比

	网络韧性与脆弱性分析	图神经网络
领域	网络分析	网络分析
方法族	Process / pipeline	Process / pipeline
起源年份	2000	2017–2018 (major variants)
提出者	Albert, Jeong & Barabási	—
类型	Network robustness / vulnerability framework	Deep learning on graph-structured data
开创性文献	Albert, R., Jeong, H. & Barabási, A.L. (2000). Error and attack tolerance of complex networks. Nature, 406, 378–382.	Kipf, T.N. & Welling, M. (2017). Semi-Supervised Classification with Graph Convolutional Networks. International Conference on Learning Representations (ICLR).
别名	network vulnerability analysis, attack tolerance analysis, Ağ Dayanıklılığı ve Güvenlik Açığı Analizi	GNN, GCN, GAT, GraphSAGE
相关	5	5
摘要	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.	A Graph Neural Network (GNN) is a deep learning architecture that operates directly on graph-structured data by combining node features with structural information through iterative neighborhood message passing. The three canonical variants — the Graph Convolutional Network (GCN) introduced by Kipf and Welling in 2017, the Graph Attention Network (GAT) introduced by Veličković et al. in 2018, and GraphSAGE — differ in how they aggregate neighbor information: GCN applies a spectral convolution over the full adjacency, GAT weights neighbors by learned attention scores, and GraphSAGE samples and aggregates local neighborhoods inductively, enabling generalization to unseen nodes.