Сравнение на методи

	Графови невронни мрежи	Случайна гора	Спектрално клъстериране
Област	Мрежови анализ	Машинно обучение	Машинно обучение
Семейство	Process / pipeline	Machine learning	Machine learning
Година на възникване	2017–2018 (major variants)	2001	2002
Създател	—	Breiman, L.	Ng, A. Y.; Jordan, M. I.; Weiss, Y.
Тип	Deep learning on graph-structured data	Ensemble (bagging of decision trees)	Graph-based clustering (spectral method)
Основополагащ източник	Kipf, T.N. & Welling, M. (2017). Semi-Supervised Classification with Graph Convolutional Networks. International Conference on Learning Representations (ICLR).	Breiman, L. (2001). Random Forests. Machine Learning, 45, 5–32.	Ng, A. Y., Jordan, M. I., & Weiss, Y. (2002). On Spectral Clustering: Analysis and an Algorithm. Advances in Neural Information Processing Systems, 14, 849–856.
Други названия	GNN, GCN, GAT, GraphSAGE	Rastgele Orman (Random Forest), rastgele orman, random decision forest, bagged tree ensemble	NJW spectral clustering, graph Laplacian clustering, normalized spectral clustering, spectral graph clustering
Свързани	5	4	5
Резюме	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.	Random Forest is an ensemble learning method, introduced by Leo Breiman in 2001, that grows many decision trees on bootstrap samples of the data and combines their votes to produce strong classification and regression. By pooling many slightly different trees, it produces more accurate and more stable predictions than any single tree.	Spectral Clustering is a graph-based unsupervised learning algorithm, formalized by Ng, Jordan, and Weiss in 2002, that maps data points into a low-dimensional eigenspace derived from the similarity graph's Laplacian before applying k-means. This spectral embedding makes it possible to recover clusters of arbitrary shape — rings, crescents, interleaved spirals — that Euclidean distance-based methods consistently fail to separate.