Disentangled Generative Graph Representation Learning

Xinyue Hu, Zhibin Duan, Bo Chen, Yuxin Li, Xinyang Liu, QuChunhui, Mingyuan Zhou

Keywords: Probabilistic Inference, disentangled representation learning, graph representation learning

Abstract:

Recently, generative graph models have shown promising results in learning graph representations through self-supervised methods. However, most existing generative graph representation learning (GRL) approaches rely on random masking across the entire graph, which overlooks the entanglement of learned representations. This oversight results in non-robustness and a lack of explainability. Furthermore, disentangling the learned representations remains a significant challenge and has not been sufficiently explored in GRL research.
Based on these insights, this paper introduces **DiGGR** (**Di**sentangled **G**enerative **G**raph **R**epresentation Learning), a self-supervised learning framework. DiGGR aims to learn latent disentangled factors and utilizes them to guide graph mask modeling, thereby enhancing the disentanglement of learned representations and enabling end-to-end joint learning.
Extensive experiments on 11 public datasets for two different graph learning tasks demonstrate that DiGGR consistently outperforms many previous self-supervised methods, verifying the effectiveness of the proposed approach.

Primary Area: Probabilistic methods (for example: variational inference, Gaussian processes)

Flagged For Ethics Review: true

Submission Number: 17865