The field of graph neural networks is moving towards more innovative and effective methods for modeling and predicting on network data. One notable direction is the improvement of graph convolutional networks, with a focus on developing more robust and adaptable methods for handling heterophilic graphs and capturing multi-scale structural patterns. Another area of advancement is in graph coarsening techniques, which aim to compress large graphs while preserving their structural and semantic integrity. These developments have the potential to enhance the performance of graph neural networks in various applications, including node classification, graph classification, and collaborative perception in distributed systems. Noteworthy papers include: Covariance Density Neural Networks, which introduces a novel approach to graph neural networks by using a density matrix as the Graph Shift Operator, allowing for enhanced discriminability and performance. Beyond Node Attention: Multi-Scale Harmonic Encoding for Feature-Wise Graph Message Passing, which proposes a new architecture that performs feature-wise adaptive message passing through node-specific harmonic projections, enabling the model to capture both smooth and oscillatory structural patterns across scales.