Semi-Supervised Classification on Non-Sparse Graphs Using Low-Rank Graph Convolutional Networks

Dominik Alfke; Martin Stoll

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arXiv · 1905.10224

Semi-Supervised Classification on Non-Sparse Graphs Using Low-Rank Graph Convolutional Networks

Dominik Alfke·Martin Stoll

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Abstract

Graph Convolutional Networks (GCNs) have proven to be successful tools for semi-supervised learning on graph-based datasets. For sparse graphs, linear and polynomial filter functions have yielded impressive results. For large non-sparse graphs, however, network training and evaluation becomes prohibitively expensive. By introducing low-rank filters, we gain significant runtime acceleration and simultaneously improved accuracy. We further propose an architecture change mimicking techniques from Model Order Reduction in what we call a reduced-order GCN. Moreover, we present how our method can also be applied to hypergraph datasets and how hypergraph convolution can be implemented efficiently.

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cs.LG cs.DM cs.NA cs.NE math.NA stat.ML

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BibTeX RIS

Dominik Alfke, Martin Stoll. 2019-05-24. Semi-Supervised Classification on Non-Sparse Graphs Using Low-Rank Graph Convolutional Networks. https://arxiv.org/abs/1905.10224

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