Graph neural networks (GNNs), a type of neural network that can learn from
graph-structured data and learn the representation of nodes by aggregating
their neighbors, have shown excellent performance in downstream tasks.However,
it is known that the performance of graph neural networks (GNNs) degrades
gradually as the number of layers increases. Based on k-hop subgraph
aggregation, which is a new concept, we propose a new perspective to understand
the expressive power of GNN.From this perspective, we reveal the potential
causes of the performance degradation of the deep traditional GNN - aggregated
subgraph overlap, and the fact that the residual-based graph neural networks in
fact exploit the aggregation results of 1 to k hop subgraphs to improve the
effectiveness.Further, we propose a new sampling-based node-level residual
module named SDF, which is shown by theoretical derivation to obtain a superior
expressive power compared to previous residual methods by using information
from 1 to k hop subgraphs more flexibly. Extensive experiments show that the
performance and efficiency of GNN with the SDF module outperform other methods.

本研究以 k-hop 子图聚合为基础，提出了一种新的 GNN 表达能力分析视角和名为 SDF 的基于采样的节点级剩余模块，理论推导和大量实验证明 SDF 模块比以前的方法具有更高的表达能力和更高的效率。