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arXiv · 2211.07518

Heterogeneous Graph Sparsification for Efficient Representation Learning

Chandan Chunduru·Chun Jiang Zhu·Blake Gains·Jinbo Bi
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Abstract

Graph sparsification is a powerful tool to approximate an arbitrary graph and has been used in machine learning over homogeneous graphs. In heterogeneous graphs such as knowledge graphs, however, sparsification has not been systematically exploited to improve efficiency of learning tasks. In this work, we initiate the study on heterogeneous graph sparsification and develop sampling-based algorithms for constructing sparsifiers that are provably sparse and preserve important information in the original graphs. We have performed extensive experiments to confirm that the proposed method can improve time and space complexities of representation learning while achieving comparable, or even better performance in subsequent graph learning tasks based on the learned embedding.

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Chandan Chunduru, Chun Jiang Zhu, Blake Gains, Jinbo Bi. 2022-11-14. Heterogeneous Graph Sparsification for Efficient Representation Learning. https://arxiv.org/abs/2211.07518

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