Graph mining methods have been investigated for various applications including financial analysis, traffica prediction, and drug discovery. Despite their great potential in benefiting humans in the real world, recent study shows that existing graph mining methods can leak private information, are vulnerable to adversarial attacks, can inherit and magnify societal bias from training data, and lack interpretability. In this course, representative graph mining models and their inner mechanisms will be discussed. Then, we will introduce the trustworthy graph mining methods in privacy, robustness, fairness, and explainability.