Graph convolutional networks (GCNs) have been proved to be very practical to
handle various graph-related tasks. It has attracted considerable research
interest to study deep GCNs, due to their potential superior performance
compared with shallow ones. However, simply increasing network depth will, on
the contrary, hurt the performance due to the over-smoothing problem. Adding
residual connection is proved to be effective for learning deep convolutional
neural networks (deep CNNs), it is not trivial when applied to deep GCNs.
Recent works proposed an initial residual mechanism that did alleviate the
over-smoothing problem in deep GCNs. However, according to our study, their
algorithms are quite sensitive to different datasets. In their setting, the
personalization (dynamic) and correlation (evolving) of how residual applies
are ignored. To this end, we propose a novel model called Dynamic evolving
initial Residual Graph Convolutional Network (DRGCN). Firstly, we use a dynamic
block for each node to adaptively fetch information from the initial
representation. Secondly, we use an evolving block to model the residual
evolving pattern between layers. Our experimental results show that our model
effectively relieves the problem of over-smoothing in deep GCNs and outperforms
the state-of-the-art (SOTA) methods on various benchmark datasets. Moreover, we
develop a mini-batch version of DRGCN which can be applied to large-scale data.
Coupling with several fair training techniques, our model reaches new SOTA
results on the large-scale ogbn-arxiv dataset of Open Graph Benchmark (OGB).
Our reproducible code is available on GitHub.

提出一种名称为动态进化的初始残余图卷积网络（DRGCN）的新型模型，通过动态和进化块建模残差演化模式来有效缓解深 GCNs 中的超平滑问题，并在各种基准数据集上优于现有的最新技术，其代码可以在 GitHub 上复现。

DRGCN: 深度图卷积网络的动态进化初始残差

DRGCN: Dynamic Evolving Initial Residual for Deep Graph Convolutional  Networks

Convolutional Neural Networks (CNNs) have been very successful at solving a
variety of computer vision tasks such as object classification and detection,
semantic segmentation, activity understanding, to name just a few. One key
enabling factor for their great performance has been the ability to train very
deep networks. Despite their huge success in many tasks, CNNs do not work well
with non-Euclidean data, which is prevalent in many real-world applications.
Graph Convolutional Networks (GCNs) offer an alternative that allows for
non-Eucledian data input to a neural network. While GCNs already achieve
encouraging results, they are currently limited to architectures with a
relatively small number of layers, primarily due to vanishing gradients during
training. This work transfers concepts such as residual/dense connections and
dilated convolutions from CNNs to GCNs in order to successfully train very deep
GCNs. We show the benefit of using deep GCNs (with as many as 112 layers)
experimentally across various datasets and tasks. Specifically, we achieve very
promising performance in part segmentation and semantic segmentation on point
clouds and in node classification of protein functions across biological
protein-protein interaction (PPI) graphs. We believe that the insights in this
work will open avenues for future research on GCNs and their application to
further tasks not explored in this paper. The source code for this work is
available at this https URL and
this https URL for PyTorch and TensorFlow
implementation respectively.

本篇论文介绍了如何将残差 / 稠密连接和扩张卷积等深度卷积神经网络中的概念应用于图卷积网络中，从而成功地训练出多达 112 层的深度图卷积网络，该方法在多个数据集和任务中展示出了非常良好的表现。