The recent advancements in graph neural networks (GNNs) have seen a significant shift towards integrating more complex graph structures and leveraging quantum computing methodologies. Researchers are exploring novel ways to enhance the expressivity and scalability of GNNs by incorporating advanced graph structures such as superhypergraphs and plithogenic graphs, which allow for more nuanced modeling of complex relationships. Additionally, the fusion of quantum computing with traditional GNNs is emerging as a promising direction, offering potential improvements in feature extraction and computational efficiency. These innovations are particularly evident in the development of quantum-enhanced pointwise convolutions and quantum-based transformers for graph representation learning. Furthermore, there is a growing emphasis on data-centric approaches, where the focus is on improving graph quality and representation, especially in the context of directed and continuous-time dynamic graphs. This shift is driven by the need to better capture real-world complexities and temporal dynamics, as seen in applications like financial market prediction. Notably, self-supervised learning frameworks are also being developed to train MLPs on graphs without supervision, aiming to integrate structural information more effectively. These developments collectively indicate a move towards more sophisticated and scalable solutions in graph-based machine learning, with a strong emphasis on both theoretical foundations and practical applications.