Augmentations in Graph Contrastive Learning: Current Methodological Flaws & Towards Better Practices
Puja Trivedi, Ekdeep Singh Lubana, Yujun Yan, Yaoqing Yang, Danai Koutra
Abstract
Graph classification has a wide range of applications in bioinformatics, social sciences, automated fake news detection, web document classification, and more. In many practical scenarios, including web-scale applications, labels are scarce or hard to obtain. Unsupervised learning is thus a natural paradigm for these settings, but its performance often lags behind that of supervised learning. However, recently contrastive learning (CL) has enabled unsupervised computer vision models to perform comparably to supervised models. Theoretical and empirical works analyzing visual CL frameworks find that leveraging large datasets and task relevant augmentations is essential for CL framework success. Interestingly, graph CL frameworks report high performance while using orders of magnitude smaller data, and employing domain-agnostic graph augmentations (DAGAs) that can corrupt task relevant information.