一种用于节点聚类的对比变分图自编码器
A Contrastive Variational Graph Auto-Encoder for Node Clustering
AI总结：
提出一种对比变分图自编码器，通过更紧的变分下界和两种控制机制，解决节点聚类中后验坍缩、特征随机性与特征漂移问题，实现最先进聚类性能。
摘要：
变分图自编码器（VGAEs）已被广泛用于解决节点聚类任务。然而，最先进的方法面临诸多挑战。首先，现有的VGAEs在引入聚类归纳偏置后，未考虑推理模型与生成模型之间的差异。其次，当前模型容易退化为使潜在编码独立于输入信号而匹配先验的解（即后验坍缩）。第三，现有的VGAEs忽视了噪声聚类分配的影响（即特征随机性）以及聚类与重建之间强权衡的影响（即特征漂移）。为解决这些问题，我们在对比设置中构建了一个变分下界。我们的下界是对数似然函数比相应的证据下界（ELBO）更紧的近似。得益于一个新识别的项，我们的下界能够避免后验坍缩，并具有更大的灵活性来考虑推理模型与生成模型之间的差异。此外，我们的解决方案有两种机制来控制特征随机性与特征漂移之间的权衡。大量实验表明，所提出的方法在多个数据集上取得了最先进的聚类结果。我们提供了强有力的证据，证明这一改进归因于四个方面：整合对比学习以及缓解特征随机性、特征漂移和后验坍缩。
英文摘要：
Variational Graph Auto-Encoders (VGAEs) have been widely used to solve the node clustering task. However, the state-of-the-art methods have numerous challenges. First, existing VGAEs do not account for the discrepancy between the inference and generative models after incorporating the clustering inductive bias. Second, current models are prone to degenerate solutions that make the latent codes match the prior independently of the input signal (i.e., Posterior Collapse). Third, existing VGAEs overlook the effect of the noisy clustering assignments (i.e., Feature Randomness) and the impact of the strong trade-off between clustering and reconstruction (i.e., Feature Drift). To address these problems, we formulate a variational lower bound in a contrastive setting. Our lower bound is a tighter approximation of the log-likelihood function than the corresponding Evidence Lower BOund (ELBO). Thanks to a newly identified term, our lower bound can escape Posterior Collapse and has more flexibility to account for the difference between the inference and generative models. Additionally, our solution has two mechanisms to control the trade-off between Feature Randomness and Feature Drift. Extensive experiments show that the proposed method achieves state-of-the-art clustering results on several datasets. We provide strong evidence that this improvement is attributed to four aspects: integrating contrastive learning and alleviating Feature Randomness, Feature Drift, and Posterior Collapse.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
