Bandingkan kaedah

	Graph Neural Network	Analisis Sentraliti	Penyematan Jaringan
Bidang	Analisis Rangkaian	Analisis Rangkaian	Analisis Rangkaian
Keluarga	Process / pipeline	Process / pipeline	Process / pipeline
Tahun asal	2017–2018 (major variants)	1979	2014 (DeepWalk); 2016 (Node2Vec)
Pengasas	—	Linton C. Freeman	—
Jenis	Deep learning on graph-structured data	Descriptive / exploratory network measure family	Representation learning / unsupervised network method
Sumber perintis	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.	Grover, A. & Leskovec, J. (2016). Node2Vec: Scalable Feature Learning for Networks. Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD), 855-864.
Alias	GNN, GCN, GAT, GraphSAGE	Merkeziyet Analizi (Degree, Betweenness, Eigenvector), node centrality, centrality measures, graph centrality	node embedding, graph embedding, network representation learning, Ağ Gömme (Node2Vec, DeepWalk, LINE)
Berkaitan	5	5	3
Ringkasan	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.	Network embedding is a family of representation-learning methods that map each node of a graph into a dense, low-dimensional vector while preserving the network's structural properties. The approach was formalised for social-network data by Perozzi, Al-Rfou, and Skiena with DeepWalk (2014), which adapted the Word2Vec skip-gram model to random walks on graphs, and extended by Grover and Leskovec with Node2Vec (2016), which introduced a biased random walk that balances breadth-first and depth-first exploration. These embeddings turn relational data into feature vectors that standard machine-learning classifiers and clustering algorithms can consume directly.