Compara mètodes

	Els nuclis de grafs	Xarxa Neuronal de Grafs
Camp	Anàlisi de xarxes	Anàlisi de xarxes
Família	Machine learning	Process / pipeline
Any d'origen	2010	2017–2018 (major variants)
Autor original	Vishwanathan, Schraudolph, Kondor & Borgwardt	—
Tipus	Positive semi-definite kernel function over graphs	Deep learning on graph-structured data
Font seminal	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).
Àlies	Structured Graph Kernels, Kernel Methods on Graphs, Graf Çekirdekleri, Graph Similarity Kernels	GNN, GCN, GAT, GraphSAGE
Relacionats	2	5
Resum	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.