Model architecture challenges
GNNs have shown remarkable success in various graph learning tasks. However, they face several architectural challenges that limit their effectiveness in certain scenarios. Here, we investigate four key model architecture challenges in graph learning.
Capturing long-range dependencies
GNNs often struggle to capture dependencies between distant nodes in the graph, as information typically propagates only to immediate neighbors in each layer. For instance, in scenarios such as citation networks, a paper might be influenced by another paper several citation links away. Standard GNNs might fail to capture this influence if it extends beyond their receptive field.
Graph attention mechanisms and higher-order graph convolutions represent two sophisticated approaches to enhancing GNNs’ long-range capabilities. Graph attention mechanisms introduce a dynamic weighting system that allows the model to intelligently focus on the most significant connections...
