مقایسهٔ روش‌ها

	شبکه عصبی گراف	تحلیل مرکزیت	تحلیل شبکه چندلایه‌ای	تعبیه شبکه
حوزه	تحلیل شبکه	تحلیل شبکه	تحلیل شبکه	تحلیل شبکه
خانواده	Process / pipeline	Process / pipeline	Process / pipeline	Process / pipeline
سال پیدایش	2017–2018 (major variants)	1979	2013–2014 (formal mathematical framework)	2014 (DeepWalk); 2016 (Node2Vec)
پدیدآور	—	Linton C. Freeman	Kivelä et al. (2014); De Domenico et al. (2013)	—
نوع	Deep learning on graph-structured data	Descriptive / exploratory network measure family	Graph-theoretic network model	Representation learning / unsupervised network method
منبع بنیادین	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.	Kivelä, M. et al. (2014). Multilayer Networks. Journal of Complex Networks, 2(3), 203–271.	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.
نام‌های دیگر	GNN, GCN, GAT, GraphSAGE	Merkeziyet Analizi (Degree, Betweenness, Eigenvector), node centrality, centrality measures, graph centrality	multiplex network analysis, multiplex networks, Çok Katmanlı Ağ Analizi (Multiplex Networks)	node embedding, graph embedding, network representation learning, Ağ Gömme (Node2Vec, DeepWalk, LINE)
مرتبط	5	5	6	3
خلاصه	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.	Multilayer network analysis is a graph-theoretic framework, formalised by Kivelä et al. (2014) and De Domenico et al. (2013), that represents the same set of nodes simultaneously across multiple relationship layers. Where a single-layer network collapses all relationships into one graph, the multilayer model preserves the distinct relational context of each layer — social platform, biological interaction type, or infrastructure tier — while also modelling how layers couple with each other through interlayer edges.	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.