CunWang520

RGLT

GLT has presented the first attempt to accelerate GNN inference. Though promising, GLT encounters robustness and generalization issues when deployed in real-world scenarios. To tackle these issues, we propose the Resilient Graph Lottery Ticket (RGLT) to find more robust and generalizable GLT in GNNs.

RegularX

This work studies the explainability of graph neural networks (GNNs), which is important for the credibility of GNNs in practical usage.

Snowhypothe

NoSAF-NoSAF-D

CunWang520/Snowhypothe

CunWang520/RGLT

GLT has presented the first attempt to accelerate GNN inference. Though promising, GLT encounters robustness and generalization issues when deployed in real-world scenarios. To tackle these issues, we propose the Resilient Graph Lottery Ticket (RGLT) to find more robust and generalizable GLT in GNNs.

CunWang520/RegularX

This work studies the explainability of graph neural networks (GNNs), which is important for the credibility of GNNs in practical usage.