The core operation of current Graph Neural Networks (GNNs) is the aggregation
enabled by the graph Laplacian or message passing, which filters the
neighborhood information of nodes. Though effective for various tasks, in this
paper, we show that they are potentially a problematic factor underlying all
GNN models for learning on certain datasets, as they force the node
representations similar, making the nodes gradually lose their identity and
become indistinguishable. Hence, we augment the aggregation operations with
their dual, i.e. diversification operators that make the node more distinct and
preserve the identity. Such augmentation replaces the aggregation with a
two-channel filtering process that, in theory, is beneficial for enriching the
node representations. In practice, the proposed two-channel filters can be
easily patched on existing GNN methods with diverse training strategies,
including spectral and spatial (message passing) methods. In the experiments,
we observe desired characteristics of the models and significant performance
boost upon the baselines on 9 node classification tasks.

本篇论文提出通过增加双通道筛选器以替换传统图神经网络中的聚合操作，从而避免节点表示逐渐失去其标识并变得不可区分，最终在 9 个节点分类任务上获得了显著的表现提升。