  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.
