Graph Convolutional Networks (GCNs) have received increasing attention in the
machine learning community for effectively leveraging both the content features
of nodes and the linkage patterns across graphs in various applications. As
real-world graphs are often incomplete and noisy, treating them as ground-truth
information, which is a common practice in most GCNs, unavoidably leads to
sub-optimal solutions. Existing efforts for addressing this problem either
involve an over-parameterized model which is difficult to scale, or simply
re-weight observed edges without dealing with the missing-edge issue. This
paper proposes a novel framework called Graph-Revised Convolutional Network
(GRCN), which avoids both extremes. Specifically, a GCN-based graph revision
module is introduced for predicting missing edges and revising edge weights
w.r.t. downstream tasks via joint optimization. A theoretical analysis reveals
the connection between GRCN and previous work on multigraph belief propagation.
Experiments on six benchmark datasets show that GRCN consistently outperforms
strong baseline methods by a large margin, especially when the original graphs
are severely incomplete or the labeled instances for model training are highly
sparse.

提出一种名为图修正卷积网络（GRCN）的框架，通过引入基于 GCN 的图修正模块来预测缺失边并在下游任务中进行边缘权重修正，以避免过度参数化或简单的重新加权观察到的边缘的问题，并且在六个基准数据集上的实验结果表明，与强基准方法相比，GRCN 始终表现出较大的优势，尤其是原始图严重不完整或用于模型训练的标记实例高度稀疏的情况。