Abstract: Graph Neural Networks (GNNs) have emerged as a promising tool to handle data exhibiting an irregular structure. However, most GNN architectures perform well on homophilic datasets, where the ...

Author Shawn Peters blends clarity and rigor to make data structures and algorithms accessible to all learners. COLORADO, CO, UNITED STATES, January 2, 2026 /EINPresswire.com/ — Vibrant Publishers ...

Abstract: Graph Convolutional Networks (GCNs) have been widely studied for semi-supervised learning tasks. It is known that the graph convolution operations in most of existing GCNs are composed of ...