Search Results for author:
Found 2 papers, 0 papers with code
Link Prediction under Heterophily: A Physics-Inspired Graph Neural Network Approach
Physics-Inspired GNNs such as GRAFF provided a significant contribution to enhance node classification performance under heterophily, thanks to the adoption of physics biases in the message-passing.
Renormalized Graph Neural Networks
It is recognized as the preferred lens through which to study complex systems, offering a framework that can untangle their intricate dynamics.
Cannot find the paper you are looking for? You can
Submit a new open access paper.
