Towards Robust Graph Contrastive Learning

2021/02/25 by Nikola Jovanović, Jovanović, Nikola, Meng Zhao +5
Computer Science · #Advanced Graph Neural Networks #Adversarial Robustness in Machine Learning #Artificial Intelligence (cs.AI) #Domain Adaptation and Few-Shot Learning #FOS: Computer and information sciences #Machine Learning (cs.LG) #Machine Learning (stat.ML) #Social and Information Networks (cs.SI)

paper · pdf · doi:10.48550/arxiv.2102.13085

openalex publication_date 2021/02/25 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28

Abstract

We study the problem of adversarially robust self-supervised learning on graphs. In the contrastive learning framework, we introduce a new method that increases the adversarial robustness of the learned representations through i) adversarial transformations and ii) transformations that not only remove but also insert edges. We evaluate the learned representations in a preliminary set of experiments, obtaining promising results. We believe this work takes an important step towards incorporating robustness as a viable auxiliary task in graph contrastive learning.

Citations

• Learning deep representations by mutual information estimation and maximization
• Fast Graph Representation Learning with PyTorch Geometric
• Detecting Functionality-Specific Vulnerabilities via Retrieving Individual Functionality-Equivalent APIs in Open-Source Repositories
• PyTorch: An Imperative Style, High-Performance Deep Learning Library
• A Simple Framework for Contrastive Learning of Visual Representations
• Deep Graph Contrastive Representation Learning

Related

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• GPS: Graph Contrastive Learning via Multi-scale Augmented Views from Adversarial Pooling
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