Defferrard, Michaël, Xavier Bresson, and Pierre Vandergheynst. "Convolutional neural networks on graphs with fast localized spectral filtering." Advances in Neural Information Processing Systems. 2016 ...

Graphs (or networks) are ubiquitous representations in life sciences and medicine, from molecular interactions maps, signaling transduction pathways, to graphs of scientific knowledge , and ...

Graph structured data such as social networks and molecular graphs are omnipresent in the real world. Developing sophisticated algorithms for representation learning on graph structured data holds ...

• Benchmarking graph-guided neural networks against traditional or other AI-based approaches • Interpretability, explainability, and domain knowledge integration We welcome Original Research articles, ...

Graph neural networks (GNNs) have achieved tremendous success on multiple graph-based learning tasks by fusing network structure and node features. Modern GNN models are built upon iterative ...

Cognitive radio (CR) facilitates efficient spectrum management and optimization in wireless networks with massive wireless access and data transmission demands, where spectrum sensing (SS) is a ...

A team of chemistry, life science, and AI researchers are using graph neural networks to identify molecules and predict smells. Models made by researchers outperform current state-of-the-art ...