Graph neural networks (GNNs) have been extensively studied for prediction
tasks on graphs. As pointed out by recent studies, most GNNs assume local
homophily, i.e., strong similarities in local neighborhoods. This assumption
however limits the generalizability power of GNNs. To address this limitation,
we propose a flexible GNN model, which is capable of handling any graphs
without being restricted by their underlying homophily. At its core, this model
adopts a node attention mechanism based on multiple learnable spectral filters;
therefore, the aggregation scheme is learned adaptively for each graph in the
spectral domain. We evaluated the proposed model on node classification tasks
over eight benchmark datasets. The proposed model is shown to generalize well
to both homophilic and heterophilic graphs. Further, it outperforms all
state-of-the-art baselines on heterophilic graphs and performs comparably with
them on homophilic graphs.

提出了一种使用多个可学习谱滤波器的节点注意力机制的柔性图神经网络（GNN）模型，将聚合方案适应性地学习到每个图的谱域，从而更好地应对任意类型的图并在节点分类任务中取得了优越成果。