论文标题
通过Sylvester多画神经网络完成几何矩阵
Geometric Matrix Completion via Sylvester Multi-Graph Neural Network
论文作者
论文摘要
尽管Sylvester方程在各种图形挖掘应用程序（例如半监督标签学习和网络对齐）上取得了成功，但仍存在一些限制。 Sylvester方程无法建模非线性关系以及对不同任务进行调整的僵化性限制了其绩效。在本文中，我们提出了一个端到端的神经框架Symgnn，该框架由多网神经聚合模块和先前的多网络协会结合学习模块组成。提出的框架继承了Sylvester方程的关键思想，同时将其推广以克服上述局限性。对现实世界数据集的经验评估表明，Symgnn总体的实例超过了几何矩阵完成任务的基准，其低级别的实例化可以将记忆消耗降低16.98 \％。
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.
