Disentangled Generative Graph Representation Learning
This work addresses the challenge of non-robustness and lack of explainability in graph representation learning, offering a novel approach for researchers in machine learning and graph analysis, though it appears incremental as it builds on existing generative methods.
The paper tackles the problem of entangled representations in generative graph models by introducing DiGGR, a self-supervised framework that learns disentangled factors to guide graph masking, resulting in improved performance over previous methods on 11 datasets for graph learning tasks.
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 (Disentangled Generative Graph Representation 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.