เปรียบเทียบวิธี

	Graph Neural Network	การวิเคราะห์ความเป็นศูนย์กลาง	การตรวจจับชุมชน
สาขาวิชา	การวิเคราะห์เครือข่าย	การวิเคราะห์เครือข่าย	การวิเคราะห์เครือข่าย
ตระกูล	Process / pipeline	Process / pipeline	Process / pipeline
ปีกำเนิด	2017–2018 (major variants)	1979	2002–2019 (algorithm family)
ผู้ริเริ่ม	—	Linton C. Freeman	Louvain: Blondel et al. (2008); Leiden: Traag et al. (2019); Girvan-Newman: Girvan & Newman (2002); Infomap: Rosvall & Bergstrom (2008)
ประเภท	Deep learning on graph-structured data	Descriptive / exploratory network measure family	Graph-partitioning / clustering algorithm family
แหล่งต้นตำรับ	Kipf, T.N. & Welling, M. (2017). Semi-Supervised Classification with Graph Convolutional Networks. International Conference on Learning Representations (ICLR).	Freeman, L.C. (1979). Centrality in Social Networks: Conceptual Clarification. Social Networks, 1(3), 215-239.	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.
ชื่อเรียกอื่น	GNN, GCN, GAT, GraphSAGE	Merkeziyet Analizi (Degree, Betweenness, Eigenvector), node centrality, centrality measures, graph centrality	graph clustering, network partitioning, Topluluk Tespiti (Louvain, Girvan-Newman, Leiden)
ที่เกี่ยวข้อง	5	5	5
สรุป	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.	Centrality analysis is a family of network-analytic measures, formalized by Freeman (1979), that quantifies the structural importance of individual nodes within a graph. Each centrality index captures a distinct mechanism of influence: degree centrality reflects direct connectivity, betweenness centrality identifies nodes that broker information flow, closeness centrality captures proximity to all others, and eigenvector centrality (along with PageRank) rewards connection to highly connected neighbors.	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?