Graph convolutional networks (GCNs) update a node's feature vector by
aggregating features from its neighbors in the graph. This ignores potentially
useful contributions from distant nodes. Identifying such useful distant
contributions is challenging due to scalability issues (too many nodes can
potentially contribute) and oversmoothing (aggregating features from too many
nodes risks swamping out relevant information and may result in nodes having
different labels but indistinguishable features). We introduce a global
attention mechanism where a node can selectively attend to, and aggregate
features from, any other node in the graph. The attention coefficients depend
on the Euclidean distance between learnable node embeddings, and we show that
the resulting attention-based global aggregation scheme is analogous to
high-dimensional Gaussian filtering. This makes it possible to use efficient
approximate Gaussian filtering techniques to implement our attention-based
global aggregation scheme. By employing an approximate filtering method based
on the permutohedral lattice, the time complexity of our proposed global
aggregation scheme only grows linearly with the number of nodes. The resulting
GCNs, which we term permutohedral-GCNs, are differentiable and trained
end-to-end, and they achieve state of the art performance on several node
classification benchmarks.

本文提出了一种具有全局注意机制的图卷积网络，能够选择性地关注和汇聚图中的任何节点，同时使用基于 Permutohedral 网络的近似高斯过滤技术实现全局汇聚方案，从而实现对节点分类性能的优化。