A Simple Training Strategy for Graph Autoencoder
A Simple Training Strategy for Graph Autoencoder
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DOI：
10.1145/3383972.3383985
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发表时间：
2020-02
期刊：
影响因子：
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通讯作者：
Yingfeng Wang;Biyun Xu;M. Kwak;Xiaoqin Zeng
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文献类型：
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作者：
Yingfeng Wang;Biyun Xu;M. Kwak;Xiaoqin Zeng
图自编码器能够将图数据映射到低维空间。它是一种强大的图嵌入方法，应用于图分析中以降低计算成本。图自编码器的训练算法搜索权重设置，以便在降维的情况下保留图数据的大部分图信息。本文提出了一种简单的训练策略，它能够在不显著增加时间复杂度的情况下提高训练性能。这种策略可以灵活地适配许多现有的训练算法。实验结果证实了该策略的有效性。
Graph autoencoder can map graph data into a low-dimensional space. It is a powerful graph embedding method applied in graph analytics to reduce the computational cost. The training algorithm of a graph autoencoder searches the weight setting for preserving most graph information of the graph data with reduced dimensionality. This paper presents a simple training strategy, which can improve the training performance without significantly increasing time complexity. This strategy can flexibly fit many existing training algorithms. The experimental results confirm the effectiveness of this strategy.