Trustworthy Graph

Trustworthy graph learning aims to build reliable, explainable, and private graph neural networks (GNNs) for various applications, addressing concerns about model robustness, fairness, and privacy violations. Current research focuses on developing GNN architectures and algorithms that are resilient to adversarial attacks and distributional shifts, while also providing interpretable predictions and mitigating biases. This field is crucial for deploying GNNs responsibly in high-stakes domains like healthcare and finance, ensuring accurate and ethical outcomes.