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Convolutional Graph-Tensor Net for Graph Data Completion

2021-03-07 23:33:38
Xiao-Yang Liu, Ming Zhu

Abstract

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.

Abstract (translated)

URL

https://arxiv.org/abs/2103.04485

PDF

https://arxiv.org/pdf/2103.04485.pdf


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