3 citations · 4 across the 2 of their papers we have counts for
2 papers
cs.LG2024★ 1 cited
Global Human-guided Counterfactual Explanations for Molecular Properties via Reinforcement Learning
Danqing Wang, Antonis Antoniades, Kha-Dinh Luong +6
Counterfactual explanations of Graph Neural Networks (GNNs) offer a powerful way to understand data that can naturally be represented by a graph structure. Furthermore, in many dom…
cs.LG2023★ 3 cited
Link Prediction without Graph Neural Networks
Zexi Huang, Mert Kosan, Arlei Silva +1
Link prediction, which consists of predicting edges based on graph features, is a fundamental task in many graph applications. As for several related problems, Graph Neural Network…