Graph data completion is a fundamentally important issue as data generally has a graph structure, e.g., social networks, recommendation systems, and the Internet of Things. We consider a graph where each node has a data matrix, represented as a \textit{graph-tensor} by stacking the data matrices in the third dimension. In this paper, we propose a \textit{Convolutional Graph-Tensor Net} (\textit{Conv GT-Net}) for the graph data completion problem, which uses deep neural networks to learn the general transform of graph-tensors. The experimental results on the ego-Facebook data sets show that the proposed \textit{Conv GT-Net} achieves significant improvements on both completion accuracy (50\% higher) and completion speed (3.6x $\sim$ 8.1x faster) over the existing algorithms.

翻译：图数据补全是一个基础且重要的问题，因为数据通常具有图结构，例如社交网络、推荐系统和物联网。本文考虑每个节点包含一个数据矩阵的图，通过将数据矩阵在第三维度上进行堆叠，将其表示为图张量。针对图数据补全问题，我们提出了一种卷积图张量网络（Conv GT-Net），该网络利用深度神经网络学习图张量的通用变换。在ego-Facebook数据集上的实验结果表明，与现有算法相比，所提出的Conv GT-Net在补全精度（提升50%）和补全速度（加速3.6倍至8.1倍）上均取得了显著提升。