GPNet: Simplifying Graph Neural Networks via Multi-channel Geometric Polynomials
This work addresses key bottlenecks in GNNs for graph-structured data, offering a simpler and more efficient model that improves performance across various network types and scales.
The paper tackles fundamental limitations in Graph Neural Networks (GNNs), such as over-smoothing and homophily assumptions, by proposing GPNet, a one-layer model that combines dilated convolution, multi-channel learning, self-attention, and sign factors, achieving competitive performance on semi-supervised and full-supervised tasks.
Graph Neural Networks (GNNs) are a promising deep learning approach for circumventing many real-world problems on graph-structured data. However, these models usually have at least one of four fundamental limitations: over-smoothing, over-fitting, difficult to train, and strong homophily assumption. For example, Simple Graph Convolution (SGC) is known to suffer from the first and fourth limitations. To tackle these limitations, we identify a set of key designs including (D1) dilated convolution, (D2) multi-channel learning, (D3) self-attention score, and (D4) sign factor to boost learning from different types (i.e. homophily and heterophily) and scales (i.e. small, medium, and large) of networks, and combine them into a graph neural network, GPNet, a simple and efficient one-layer model. We theoretically analyze the model and show that it can approximate various graph filters by adjusting the self-attention score and sign factor. Experiments show that GPNet consistently outperforms baselines in terms of average rank, average accuracy, complexity, and parameters on semi-supervised and full-supervised tasks, and achieves competitive performance compared to state-of-the-art model with inductive learning task.