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<p>Graph variational autoencoders (VAEs) have been widely used to address the representation problem of graph nodes. However, most existing graph VAEs focus on minimizing reconstruction loss and overlook the uncertainty in the latent distribution and the issue of posterior collapse during training. An Adversarial Regularize Graph Variational Autoencoder Based on Encoder Optimization Strategy (MCM-ARVGE) is proposed from the perspective of network structure and loss function. MCM-ARVGE introduces a Multi-dimensional Cloud Generator (MCG) that transforms the traditional encoder, expanding the Gaussian distribution into a Gaussian cloud distribution. Furthermore, MCM-ARVGE employs the idea of adversarial regularization to train the Gaussian cloud distribution, reducing the randomness of the Gaussian cloud distribution. Finally, based on the Gaussian cloud distribution, an effective uncertainty similarity measurement method for cloud distributions is introduced to address the problem of posterior collapse. Experimental results validate the universality and effectiveness of MCM-ARVGE, as it outperforms the baseline model in graph embedding tasks.</p>

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Adversarial regularize graph variational autoencoder based on encoder optimization strategy

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2025年1月6日

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  • Jin Dai,
  • Yanhui Peng,
  • Guoyin Wang,
  • Feng Hu

摘要

Graph variational autoencoders (VAEs) have been widely used to address the representation problem of graph nodes. However, most existing graph VAEs focus on minimizing reconstruction loss and overlook the uncertainty in the latent distribution and the issue of posterior collapse during training. An Adversarial Regularize Graph Variational Autoencoder Based on Encoder Optimization Strategy (MCM-ARVGE) is proposed from the perspective of network structure and loss function. MCM-ARVGE introduces a Multi-dimensional Cloud Generator (MCG) that transforms the traditional encoder, expanding the Gaussian distribution into a Gaussian cloud distribution. Furthermore, MCM-ARVGE employs the idea of adversarial regularization to train the Gaussian cloud distribution, reducing the randomness of the Gaussian cloud distribution. Finally, based on the Gaussian cloud distribution, an effective uncertainty similarity measurement method for cloud distributions is introduced to address the problem of posterior collapse. Experimental results validate the universality and effectiveness of MCM-ARVGE, as it outperforms the baseline model in graph embedding tasks.