Missing node attributes pose a common problem in real-world graphs, impacting the performance of graph neural networks’ representation learning. Existing GNNs often struggle to effectively leverage ...

Graph neural network architectures are tested for their ability to generalize using multiple data set splits, including out-of-distribution HFEs and unseen molecular scaffolds. Our most important ...

In GIGNet, multi-level graph neural networks (GNNs) are utilized to extract internal graph-based features from signal samples and correlation information between different signals treated as nodes in ...