3 citations · 4 across the 5 of their papers we have counts for
5 papers · 1 filter
ATEX-CF: Attack-Informed Counterfactual Explanations for Graph Neural Networks
Yu Zhang, Sean Bin Yang, Arijit Khan +1
Counterfactual explanations offer an intuitive way to interpret graph neural networks (GNNs) by identifying minimal changes that alter a model's prediction, thereby answering "what…
Finding Counterfactual Evidences for Node Classification
Dazhuo Qiu, Jinwen Chen, Arijit Khan +2
Counterfactual learning is emerging as an important paradigm, rooted in causality, which promises to alleviate common issues of graph neural networks (GNNs), such as fairness and i…
SliceGX: Layer-wise GNN Explanation with Model-slicing
Tingting Zhu, Tingyang Chen, Yinghui Wu +2
Ensuring the trustworthiness of graph neural networks (GNNs), which are often treated as black-box models, requires effective explanation techniques. Existing GNN explanations typi…
Interpreting Graph Inference with Skyline Explanations
Dazhuo Qiu, Haolai Che, Arijit Khan +1
Inference queries have been routinely issued to graph machine learning models such as graph neural networks (GNNs) for various network analytical tasks. Nevertheless, GNN outputs a…
Generating Robust Counterfactual Witnesses for Graph Neural Networks
Dazhuo Qiu, Mengying Wang, Arijit Khan +1
This paper introduces a new class of explanation structures, called robust counterfactual witnesses (RCWs), to provide robust, both counterfactual and factual explanations for grap…