The field of graph learning is moving towards developing more adaptable and fair graph neural networks (GNNs). Recent studies have focused on improving the adaptability of pre-trained GNNs to downstream tasks through graph prompting, which involves modifying input graph data with learnable prompts. Additionally, there is a growing emphasis on addressing fairness concerns in GNNs, such as mitigating bias in node representations and ensuring proportional representation across sensitive groups. Noteworthy papers in this area include GraphTOP, which proposes a pioneering graph topology-oriented prompting framework, and Adaptive Dual Prompting, which enhances fairness for adapting pre-trained GNN models to downstream tasks. Other notable papers include Cross-Paradigm Graph Backdoor Attacks with Promptable Subgraph Triggers, A Deep Latent Factor Graph Clustering with Fairness-Utility Trade-off Perspective, and Learning Fair Graph Representations with Multi-view Information Bottleneck, which all contribute to advancing the field of graph learning in terms of fairness, adaptability, and robustness.