# Convolutional Graph-Tensor Net for Graph Data Completion

arXiv: https://arxiv.org/abs/2103.04485 · HTML: https://mlsanity.com/paper/2103.04485/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 4 of its 16 references that are in the map.
- First posted on arXiv 7 Mar 2021.

## Builds on (most cited first, 4 of 4)

- [Deep Residual Learning for Image Recognition](https://mlsanity.com/paper/1512.03385/) (2015)
- [Adam: A Method for Stochastic Optimization](https://mlsanity.com/paper/1412.6980/) (2014)
- [Novel methods for multilinear data completion and de-noising based on tensor-SVD](https://mlsanity.com/paper/1407.1785/) (2014)
- [Low-tubal-rank Tensor Completion using Alternating Minimization](https://mlsanity.com/paper/1610.01690/) (2016)

## Abstract (arXiv)

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 textitgraph-tensor by stacking the data matrices in the third dimension. In this paper, we propose a textitConvolutional Graph-Tensor Net (textitConv 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 textitConv GT-Net achieves significant improvements on both completion accuracy (50% higher) and completion speed (3.6x ~ 8.1x faster) over the existing algorithms.