قارن الطرق

	نموذج الكتل العشوائية (Stochastic Block Model	شبكة الانتباه الرسومية	التجميع الهرمي
المجال	تحليل الشبكات	التعلم العميق	تعلم الآلة
العائلة	Process / pipeline	Machine learning	Machine learning
سنة النشأة	1983	2018	1963
صاحب الطريقة	—	Veličković, P. et al.	Ward, J. H.
النوع	Probabilistic generative graph model	Graph neural network (attention-based)	Unsupervised clustering (agglomerative)
المصدر التأسيسي	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.	Ward, J. H. (1963). Hierarchical Grouping to Optimize an Objective Function. Journal of the American Statistical Association, 58(301), 236–244.
الأسماء البديلة	SBM, degree-corrected SBM, DCSBM, Stokastik Blok Modeli (SBM)	Graf Dikkat Ağı (GAT), GAT, graph attention network, attention-based graph neural network	Hiyerarşik Kümeleme, hiyerarşik kümeleme, agglomerative clustering, hierarchical agglomerative clustering
ذات صلة	7	4	4
الملخص	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).	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.