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.
Fraud Detection System using Graph Neural Networks (AD-RL-GNN) to identify complex fraud patterns. Features: 22.7% G-Means improvement over baseline, <28ms real-time latency, Adaptive Majority Downsampling (MCD) for 28:1 class imbalance, and a scalable MLOps pipeline (FastAPI, Redis, Docker).
Relational Deep Learning and Explainability of Graph Neural Network
A Python library for building AI agents that leverage the full power of Google Antigravity.
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