ProtoMGAE: Prototype-aware Masked Graph Auto-Encoder for Graph Representation Learning

What is it about?

In this paper, we propose a graph representation learning method based on Prototype-aware contrastive learning and Masked Graph Auto-Encoding, named ProtoMGAE. Our model leverages three complementary objectives, i.e., masked feature reconstruction, clustering consistency, and representation contrasting, to capture graph information and learn node representations from macro, meso, and micro perspectives.

Featured Image

Photo by Conny Schneider on Unsplash

Photo by Conny Schneider on Unsplash

Why is it important?

Enhanced representations. We employ a masked graph modeling strategy to accommodate incomplete graphs with missing node features. Moreover, the contrastive objective of the online-target network pulls positive pairs aligned closely while ensuring the representations are uniformly distributed on the unit hypersphere. These strategies help us learn more robust and discriminative node representations. Performance improvement. Extensive experiments conducted on several datasets demonstrate that the proposed method achieves significantly better or competitive performance on downstream tasks, especially for graph clustering, compared with the state-of-the-art methods, showcasing its superiority in enhancing graph representation learning.

Perspectives