A Contrastive Variational Graph Auto-Encoder for Node Clustering

• URL: http://arxiv.org/abs/2312.16830v1
• Date: Thu, 28 Dec 2023 05:07:57 GMT
• Title: A Contrastive Variational Graph Auto-Encoder for Node Clustering
• Authors: Nairouz Mrabah, Mohamed Bouguessa, Riadh Ksantini
• Abstract summary: State-of-the-art clustering methods have numerous challenges. Existing VGAEs do not account for the discrepancy between the inference and generative models. Our solution has two mechanisms to control the trade-off between Feature Randomness and Feature Drift.
• Score: 10.52321770126932
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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.

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