Comparar métodos

	Rede Neural em Grafos	Centralidade PageRank
Área	Análise de redes	Análise de redes
Família	Process / pipeline	Machine learning
Ano de origem	2017–2018 (major variants)	1999
Autor original	—	Page, Brin, Motwani & Winograd
Tipo	Deep learning on graph-structured data	Iterative link-based centrality algorithm
Fonte seminal	Kipf, T.N. & Welling, M. (2017). Semi-Supervised Classification with Graph Convolutional Networks. International Conference on Learning Representations (ICLR).	Page, L., Brin, S., Motwani, R., & Winograd, T. (1999). The PageRank citation ranking: Bringing order to the web. Stanford InfoLab Technical Report.
Outros nomes	GNN, GCN, GAT, GraphSAGE	Google PageRank, Random Surfer Model, Link-Based Ranking, PageRank Merkeziliği
Relacionados	5	2
Resumo	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.	PageRank is a link-based centrality algorithm that assigns an importance score to each node in a directed graph by measuring how many high-quality nodes point to it. Introduced by Larry Page, Sergey Brin, Rajeev Motwani, and Terry Winograd at Stanford University in 1999, it became the mathematical foundation of the Google search engine and remains one of the most influential algorithms in network science and information retrieval.