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.

Relational Deep Learning and Explainability of Graph Neural Network

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.