Reinforced Causal Explainer for Graph Neural Networks, TPAMI2022

Introduction of RGCNExplainer, an explainability approach for Relational Graph Convolutional Neural Networks.

EDGE, "Evaluation of Diverse Knowledge Graph Explanations", is a framework to benchmark diverse explanations (e.g., subgraph vs logical) for node classification in knowledge graphs.

System for detecting fake news and suggesting credible alternatives. Takes a news URL and outputs a credibility score, explanation, and top reliable sources. Uses TF-IDF + Logistic Regression, XGBoost, and DistilBERT with hybrid BERT–LightGCN models, plus SHAP and GNNExplainer for interpretability.

Relational Deep Learning and Explainability of Graph Neural Network