Abstract: Graph Transformers, emerging as a new architecture for graph representation learning, suffer from the quadratic complexity and can only handle graphs with at most thousands of nodes. To this ...
Abstract: Empowered by their remarkable advantages, graph neural networks (GNN) serve as potent tools for embedding graph-structured data and finding applications across various domains. Particularly, ...
The code accompanies paper Graph reduction with spectral and cut guarantees by Andreas Loukas published at JMLR/2019. Can one reduce the size of a graph without significantly altering its basic ...
Some results have been hidden because they may be inaccessible to you
Show inaccessible results