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arXiv · 2206.09477

Geometric Matrix Completion via Sylvester Multi-Graph Neural Network

Boxin Du·Changhe Yuan·Fei Wang·Hanghang Tong
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Abstract

Despite the success of the Sylvester equation empowered methods on various graph mining applications, such as semi-supervised label learning and network alignment, there also exists several limitations. The Sylvester equation's inability of modeling non-linear relations and the inflexibility of tuning towards different tasks restrict its performance. In this paper, we propose an end-to-end neural framework, SYMGNN, which consists of a multi-network neural aggregation module and a prior multi-network association incorporation learning module. The proposed framework inherits the key ideas of the Sylvester equation, and meanwhile generalizes it to overcome aforementioned limitations. Empirical evaluations on real-world datasets show that the instantiations of SYMGNN overall outperform the baselines in geometric matrix completion task, and its low-rank instantiation could further reduce the memory consumption by 16.98\% on average.

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Boxin Du, Changhe Yuan, Fei Wang, Hanghang Tong. 2022-06-19. Geometric Matrix Completion via Sylvester Multi-Graph Neural Network. https://arxiv.org/abs/2206.09477

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