Self-Supervised Learning of Graph Embeddings via Contrastive Predictive Coding

Abstract

Existing self-supervised learning methods for graph embeddings often rely on heuristic approaches without a robust theoretical underpinning. This work introduces a novel framework for learning graph embeddings based on contrastive predictive coding (CPC). The core idea is to train a neural network to predict future graph states given their past states, utilizing a contrastive loss function to promote similarity between embeddings of structurally similar graphs. This approach offers a more principled and stable learning process compared to existing methods. We demonstrate the effectiveness of our approach through a theoretical analysis and outline the key components and operational details. The primary contribution lies in establishing a clear connection between predictive modeling and graph embedding learning, providing a foundation for future research in this area. The proposed method aims to generate embeddings that capture the underlying structural information of graphs effectively, ultimately improving performance in downstream graph-related tasks. ---