GMNI: Achieve good data augmentation in unsupervised graph contrastive learning

Abstract

Graph contrastive learning (GCL) shows excellent potential in unsupervised graph representation learning. Data augmentation (DA), responsible for generating diverse views, plays a vital role in GCL, and its optimal choice heavily depends on the downstream task. However, it is impossible to measure task-relevant information under an unsupervised setting. Therefore, many GCL methods risk insufficient information by failing to preserve essential information necessary for the downstream task or risk encoding redundant information. In this paper, we propose a novel method called Minimal Noteworthy Information for unsupervised Graph contrastive learning (GMNI), featuring automated DA. It achieves good DA by balancing missing and excessive information, approximating the optimal views in contrastive learning. We employ an adversarial training strategy to generate views that share minimal noteworthy information (MNI), reducing nuisance information by minimization optimization and ensuring sufficient information by emphasizing noteworthy information. Besides, we introduce randomness based on MNI to augmentation, thereby enhancing view diversity and stabilizing the model against perturbations. Extensive experiments on unsupervised and semi-supervised learning over 14 datasets demonstrate the superiority of GMNI over GCL methods with automated and manual DA. GMNI achieves up to a 1.64% improvement over the state-of-the-art in unsupervised node classification, up to a 1.97% improvement in unsupervised graph classification, and up to a 3.57% improvement in semi-supervised graph classification.