Recent advances in Graph Convolutional Neural Networks (GCNNs) have shown their efficiency for nonEuclidean data on graphs, which often require a large amount of labeled data with high cost. It it ...

Graph Signal Processing in Python. Contribute to epfl-lts2/pygsp development by creating an account on GitHub.

Official implementation of Discrete Diffusion Schrödinger Bridge Matching for Graph Transformation by Jun Hyeong Kim*, Seonghwan Kim*, Seokhyun Moon*, Hyeongwoo Kim*, Jeheon Woo*, Woo Youn Kim. [arXiv ...

Serena Williams admitted to using a weight-loss medication to help her lose more than 30 pounds in her shocking body transformation.

Existing message passing-based and transformer-based graph neural networks (GNNs) cannot satisfy requirements for learning representative graph embeddings due to restricted receptive fields, redundant ...