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Graph Neural Networks (GNNs) are a promising deep learning approach forcircumventing many real-world problems on graph-structured data. However, thesemodels usually have at least one of four fundamental limitations:over-smoothing, over-fitting, difficult to train, and strong homophilyassumption. For example, Simple Graph Convolution (SGC) is known to suffer fromthe first and fourth limitations. To tackle these limitations, we identify aset of key designs including (D1) dilated convolution, (D2) multi-channellearning, (D3) self-attention score, and (D4) sign factor to boost learningfrom 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 themodel and show that it can approximate various graph filters by adjusting theself-attention score and sign factor. Experiments show that GPNet consistentlyoutperforms baselines in terms of average rank, average accuracy, complexity,and parameters on semi-supervised and full-supervised tasks, and achievescompetitive performance compared to state-of-the-art model with inductivelearning task.

GPNet: Simplifying Graph Neural Networks via Multi-channel Geometric Polynomials