Search Results for author:
Found 4 papers, 2 papers with code
On the Two Sides of Redundancy in Graph Neural Networks
Message passing neural networks iteratively generate node embeddings by aggregating information from neighboring nodes.
Gradual Weisfeiler-Leman: Slow and Steady Wins the Race
The classical Weisfeiler-Leman algorithm aka color refinement is fundamental for graph learning with kernels and neural networks.
TUDataset: A collection of benchmark datasets for learning with graphs
We provide Python-based data loaders, kernel and graph neural network baseline implementations, and evaluation tools.
Computing Optimal Assignments in Linear Time for Approximate Graph Matching
In this paper, we develop an algorithm which can find an optimal assignment in linear time when the cost function between objects is represented by a tree distance.
Cannot find the paper you are looking for? You can
Submit a new open access paper.
