방법 비교

	네트워크 복원력 및 취약성 분석	커뮤니티 탐지	그래프 신경망
분야	네트워크 분석	네트워크 분석	네트워크 분석
계열	Process / pipeline	Process / pipeline	Process / pipeline
기원 연도	2000	2002–2019 (algorithm family)	2017–2018 (major variants)
창시자	Albert, Jeong & Barabási	Louvain: Blondel et al. (2008); Leiden: Traag et al. (2019); Girvan-Newman: Girvan & Newman (2002); Infomap: Rosvall & Bergstrom (2008)	—
유형	Network robustness / vulnerability framework	Graph-partitioning / clustering algorithm family	Deep learning on graph-structured data
원전	Albert, R., Jeong, H. & Barabási, A.L. (2000). Error and attack tolerance of complex networks. Nature, 406, 378–382.	Blondel, V.D., Guillaume, J.-L., Lambiotte, R. & Lefebvre, E. (2008). Fast Unfolding of Communities in Large Networks. Journal of Statistical Mechanics, 2008(10), P10008.	Kipf, T.N. & Welling, M. (2017). Semi-Supervised Classification with Graph Convolutional Networks. International Conference on Learning Representations (ICLR).
별칭	network vulnerability analysis, attack tolerance analysis, Ağ Dayanıklılığı ve Güvenlik Açığı Analizi	graph clustering, network partitioning, Topluluk Tespiti (Louvain, Girvan-Newman, Leiden)	GNN, GCN, GAT, GraphSAGE
관련	5	5	5
요약	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.	Community detection is a family of graph-partitioning algorithms that discover densely connected sub-groups — communities — within a network. First formalised through the modularity measure by Girvan and Newman (2002), the field advanced rapidly with the Louvain method (Blondel et al., 2008), the Leiden refinement (Traag et al., 2019), and the information-theoretic Infomap approach. All variants answer the same question: which nodes cluster together more tightly among themselves than with the rest of the network?	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.