The field of graph learning is moving towards the development of more efficient and effective algorithms for handling complex graph structures. One of the key directions is the use of multi-view graph learning, which allows for the capture of multiple scales and types of interactions in a graph. This approach has been shown to be particularly effective in domains such as molecular property prediction and cosmological parameter inference. Another important area of research is the development of privacy-preserving graph learning algorithms, which are essential for applications where sensitive information is involved. Notable papers in this area include the Multi-View Graph Learning with Graph-Tuple framework, which introduces a heterogeneous message-passing architecture for learning multi-view representations from graph-tuples. The Multi-View Graph Feature Propagation for Privacy Preservation and Feature Sparsity framework is also noteworthy, as it enhances node classification under feature sparsity while promoting privacy preservation. Additionally, the Secure Sparse Matrix Multiplications and their Applications to Privacy-Preserving Machine Learning paper proposes MPC algorithms to multiply secret sparse matrices, which can significantly reduce communication costs for realistic problem sizes. Overall, the field is moving towards the development of more sophisticated and privacy-aware graph learning algorithms, with a focus on applications in scientific domains and privacy-preserving machine learning.