Edges and nodes form the core elements of heterogeneous graphs (HGs). However, existing heterogeneous graph neural networks (HGNNS) largely rely on meta-paths to capture semantic information of nodes, ...

The code for the article 'Cox-Sage: Enhancing Cox Proportional Hazards Model with Interpretable Graph Neural Networks for Cancer Prognosis' under review in Briefings in Bioinformatics - beeeginner/Cox ...

Red pixels increase the model's output while blue pixels decrease the output. The input images are shown on the left, and as nearly transparent grayscale backings behind each of the explanations. The ...

Graph Neural Networks (GNNs), particularly Graph Convolutional Neural Networks (GCNNs), have emerged as pivotal instruments in machine learning and signal processing for processing graph-structured ...