The field of Graph Neural Networks (GNNs) is moving towards addressing key challenges such as calibration, explainability, and bias mitigation. Researchers are exploring innovative approaches to improve the reliability and transparency of GNNs, including the development of unified calibration frameworks and comprehensive explainers. Additionally, there is a growing focus on understanding and mitigating model bias in GNNs, particularly in the presence of class imbalance. Theoretical analyses are also shedding light on the optimization landscape of deep GNNs, highlighting the importance of considering backward oversmoothing and its impact on training. Notable papers in this area include: The Final Layer Holds the Key, which proposes a simple yet efficient graph calibration method, and Towards Comprehensive and Prerequisite-Free Explainer for Graph Neural Networks, which introduces a novel explainer that can capture the complete decision logic of GNNs. NeuBM is also noteworthy for its approach to mitigating model bias in GNNs through neutral input calibration. Oversmoothing, Oversquashing, Heterophily, Long-Range, and more: Demystifying Common Beliefs in Graph Machine Learning provides a critical examination of common beliefs in the field, while Backward Oversmoothing: why is it hard to train deep Graph Neural Networks offers valuable insights into the optimization landscape of deep GNNs.