Сравнение методов

	Графовые ядра	Графовая нейронная сеть
Область	Сетевой анализ	Сетевой анализ
Семейство	Machine learning	Process / pipeline
Год появления	2010	2017–2018 (major variants)
Автор метода	Vishwanathan, Schraudolph, Kondor & Borgwardt	—
Тип	Positive semi-definite kernel function over graphs	Deep learning on graph-structured data
Основополагающий источник	Vishwanathan, S. V. N., Schraudolph, N. N., Kondor, R., & Borgwardt, K. M. (2010). Graph kernels. Journal of Machine Learning Research, 11, 1201–1242.	Kipf, T.N. & Welling, M. (2017). Semi-Supervised Classification with Graph Convolutional Networks. International Conference on Learning Representations (ICLR).
Другие названия	Structured Graph Kernels, Kernel Methods on Graphs, Graf Çekirdekleri, Graph Similarity Kernels	GNN, GCN, GAT, GraphSAGE
Связанные	2	5
Сводка	Graph kernels are positive semi-definite kernel functions that measure the similarity between two graphs by comparing their shared substructures — such as random walks, shortest paths, or subtree patterns. Introduced in a unified framework by Vishwanathan, Schraudolph, Kondor, and Borgwardt (2010), they bridge kernel methods and graph-structured data, enabling algorithms like SVMs to operate directly on graphs without requiring an explicit vectorization step.	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.