Choong Jun Jin, Liu Xin, Murata Tsuyoshi
Department of Computer Science, Tokyo Institute of Technology, Tokyo 152-8552, Japan.
National Institute of Advanced Industrial Science and Technology, Tokyo 135-0064, Japan.
Entropy (Basel). 2020 Feb 7;22(2):197. doi: 10.3390/e22020197.
Variational Graph Autoencoder (VGAE) has recently gained traction for learning representations on graphs. Its inception has allowed models to achieve state-of-the-art performance for challenging tasks such as link prediction, rating prediction, and node clustering. However, a fundamental flaw exists in Variational Autoencoder (VAE)-based approaches. Specifically, merely minimizing the loss of VAE increases the deviation from its primary objective. Focusing on Variational Graph Autoencoder for Community Detection (VGAECD) we found that optimizing the loss using the stochastic gradient descent often leads to sub-optimal community structure especially when initialized poorly. We address this shortcoming by introducing a dual optimization procedure. This procedure aims to guide the optimization process and encourage learning of the primary objective. Additionally, we linearize the encoder to reduce the number of learning parameters. The outcome is a robust algorithm that outperforms its predecessor.
变分图自动编码器（VGAE）最近在学习图的表示方面受到了关注。它的出现使模型能够在链路预测、评分预测和节点聚类等具有挑战性的任务中取得领先水平的性能。然而，基于变分自动编码器（VAE）的方法存在一个根本缺陷。具体而言，仅仅最小化VAE的损失会增加与其主要目标的偏差。针对用于社区检测的变分图自动编码器（VGAECD），我们发现使用随机梯度下降优化损失通常会导致次优的社区结构，尤其是在初始化较差时。我们通过引入双重优化过程来解决这一缺点。该过程旨在指导优化过程并鼓励对主要目标的学习。此外，我们将编码器线性化以减少学习参数的数量。结果是一种比其前身更强大的算法。
