Self-Supervised Learning for Discovering Graph Embeddings

Abstract

Graph neural networks (GNNs) have achieved significant success in various graph-related tasks, including node classification, link prediction, and graph classification. However, a critical limitation of many GNN approaches is their dependence on large amounts of labeled data for training. Obtaining such labeled data can be costly, time-consuming, and often impractical, particularly for large and complex graphs. This paper proposes a novel approach to learning graph embeddings using self-supervised learning (SSL). We hypothesize that intrinsic relationships within a graph structure can be leveraged to learn informative embeddings without relying on explicit labels. Our method utilizes self-supervised tasks designed to exploit the graph's connectivity and structure. Specifically, we explore techniques like contrastive learning and masked node prediction to learn embeddings that capture the underlying graph topology. We demonstrate that our self-supervised approach can learn effective graph embeddings, achieving comparable or superior performance compared to traditional supervised methods when labeled data is scarce. The key contributions of this work are the application of SSL to graph embedding learning and the design of novel self-supervised tasks tailored for graph data.