The recent advancements in graph neural networks (GNNs) have seen a significant focus on addressing privacy concerns, fairness, and efficiency in model training and inference. Researchers are increasingly developing methods to protect sensitive data from being inferred through shared models, with innovative approaches like diffusion models for link stealing attacks and efficient risk assessment techniques for graph property inference. Additionally, there is a growing emphasis on ensuring fairness in GNN predictions, particularly in community-level bias, with new frameworks like ComFairGNN that aim to mitigate biases arising from local neighborhood distributions. The field is also witnessing a critical examination of fairness in generative mobility models, highlighting the need for equity considerations in model performance across different geographic regions. These developments collectively push the boundaries of GNN applications while ensuring ethical and secure practices.