مقایسهٔ روش‌ها

	مدل بلوک تصادفی	شبکه توجه گراف	شبکه عصبی گراف	خوشه‌بندی سلسله‌مراتبی
حوزه	تحلیل شبکه	یادگیری عمیق	یادگیری عمیق	یادگیری ماشین
خانواده	Process / pipeline	Machine learning	Machine learning	Machine learning
سال پیدایش	1983	2018	2017	1963
پدیدآور	—	Veličković, P. et al.	Kipf, T.N. & Welling, M.	Ward, J. H.
نوع	Probabilistic generative graph model	Graph neural network (attention-based)	Deep learning on graph-structured data	Unsupervised clustering (agglomerative)
منبع بنیادین	Holland, P.W., Laskey, K.B. & Leinhardt, S. (1983). Stochastic Blockmodels: First Steps. Social Networks, 5(2), 109-137.	Veličković, P. et al. (2018). Graph Attention Networks. ICLR.	Kipf, T.N. & Welling, M. (2017). Semi-Supervised Classification with Graph Convolutional Networks. ICLR.	Ward, J. H. (1963). Hierarchical Grouping to Optimize an Objective Function. Journal of the American Statistical Association, 58(301), 236–244.
نام‌های دیگر	SBM, degree-corrected SBM, DCSBM, Stokastik Blok Modeli (SBM)	Graf Dikkat Ağı (GAT), GAT, graph attention network, attention-based graph neural network	Grafik Sinir Ağı (GNN), GNN, graph neural net, graph convolutional network	Hiyerarşik Kümeleme, hiyerarşik kümeleme, agglomerative clustering, hierarchical agglomerative clustering
مرتبط	7	4	4	4
خلاصه	The Stochastic Block Model (SBM), introduced by Holland, Laskey and Leinhardt (1983), is a probabilistic generative model for graphs that assigns nodes to latent blocks and parametrically estimates the connection probabilities between blocks. It is the foundational approach for community detection, core-periphery identification, and hierarchical structure discovery in network analysis.	The Graph Attention Network (GAT), introduced by Veličković and colleagues in 2018, is a graph neural network variant that learns how much importance to assign to each neighbouring node through a self-attention mechanism. On heterogeneous neighbourhoods and relational classification it produces results superior to graph convolutional networks (GCN).	A Graph Neural Network (GNN) is a deep learning method, popularised by Kipf and Welling in 2017 with the Graph Convolutional Network, that learns from the relationships in network (graph) structures made of nodes and edges. It is designed for data that is naturally relational, such as social networks, molecular structures, and recommendation systems.	Hierarchical clustering is an unsupervised method that groups observations into nested clusters and draws the result as a dendrogram, so the number of clusters need not be fixed in advance. Its agglomerative form rests on the objective-function grouping criterion introduced by Joe Ward in 1963.