@inproceedings{
zhou2024deep,
title={Deep Graph Neural Networks via Posteriori-Sampling-based Node-Adaptative Residual Module},
author={Jingbo Zhou and Yixuan Du and Ruqiong Zhang and Jun Xia and Zhizhi Yu and Zelin Zang and Di Jin and Carl Yang and Rui Zhang and Stan Z. Li},
booktitle={The Thirty-eighth Annual Conference on Neural Information Processing Systems},
year={2024},
url={https://openreview.net/forum?id=VywZsAGhp0}
}
Graph Neural Networks (GNNs), a type of neural network that can learn from graph-structured data through neighborhood information aggregation, have shown superior performance in various downstream tasks. However, as the number of layers increases, node representations becomes indistinguishable, which is known as over-smoothing. To address this issue, many residual methods have emerged. In this paper, we focus on the over-smoothing issue and related residual methods. Firstly, we revisit over-smoothing from the perspective of overlapping neighborhood subgraphs, and based on this, we explain how residual methods can alleviate over-smoothing by integrating multiple orders neighborhood subgraphs to avoid the indistinguishability of the single high-order neighborhood subgraphs. Additionally, we reveal the drawbacks of previous residual methods, such as the lack of node adaptability and severe loss of high-order neighborhood subgraph information, and propose a \textbf{Posterior-Sampling-based, Node-Adaptive Residual module (PSNR)}. We theoretically demonstrate that PSNR can alleviate the drawbacks of previous residual methods. Furthermore, extensive experiments verify the superiority of the PSNR module in fully observed node classification and missing feature scenarios. Our code
is available at \href{https://github.com/jingbo02/PSNR-GNN}{https://github.com/jingbo02/PSNR-GNN}.
Deep Graph Neural Networks via Posteriori-Sampling-based Node-Adaptative Residual Module
Jingbo Zhou Yixuan Du Ruqiong Zhang Jun Xia Zhizhi Yu Zelin Zang Di Jin Carl Yang Rui Zhang Stan Z. Li