Self-supervised Learning on Graphs: Contrastive, Generative,or Predictive (2105.07342v4)

Abstract: Deep learning on graphs has recently achieved remarkable success on a variety of tasks, while such success relies heavily on the massive and carefully labeled data. However, precise annotations are generally very expensive and time-consuming. To address this problem, self-supervised learning (SSL) is emerging as a new paradigm for extracting informative knowledge through well-designed pretext tasks without relying on manual labels. In this survey, we extend the concept of SSL, which first emerged in the fields of computer vision and natural language processing, to present a timely and comprehensive review of existing SSL techniques for graph data. Specifically, we divide existing graph SSL methods into three categories: contrastive, generative, and predictive. More importantly, unlike other surveys that only provide a high-level description of published research, we present an additional mathematical summary of existing works in a unified framework. Furthermore, to facilitate methodological development and empirical comparisons, we also summarize the commonly used datasets, evaluation metrics, downstream tasks, open-source implementations, and experimental study of various algorithms. Finally, we discuss the technical challenges and potential future directions for improving graph self-supervised learning. Latest advances in graph SSL are summarized in a GitHub repository https://github.com/LirongWu/awesome-graph-self-supervised-learning.

• The paper presents a unified mathematical framework categorizing graph SSL methods into contrastive, generative, and predictive paradigms.
• It demonstrates how self-supervised techniques reduce expensive label dependency while improving representation learning on complex graph data.
• Its comprehensive resource compilation and discussion of future challenges offer actionable insights for advancing graph machine learning research.

Self-supervised Learning on Graphs: Contrastive, Generative, or Predictive

The paper "Self-supervised Learning on Graphs: Contrastive, Generative, or Predictive" by Lirong Wu et al., provides a comprehensive survey of self-supervised learning (SSL) techniques applied to graph data. The intent of this work is to address the significant dependency on labeled data in deep learning for graphs by leveraging SSL methodologies, thereby reducing reliance on costly and labor-intensive annotations.

Overview

In this survey, the authors categorize existing graph SSL methods into three primary paradigms: contrastive, generative, and predictive. They extend SSL principles, originally established in domains such as computer vision and NLP, to graph data. A distinctive feature of this paper is its mathematical encapsulation of SSL methods, which unifies the frameworks used across current research efforts.

Key Contributions

1. Categorization of SSL Methods:
   • Contrastive SSL: These methods involve contrasting different graph augmentations or prediction tasks to learn robust representations.
   • Generative SSL: Approaches that generate graph structures or node features as pretext tasks to learn embeddings.
   • Predictive SSL: Strategies focused on predicting node attributes or link existence in a fashion that enhances feature learning without explicit labels.
2. Unified Mathematical Framework: The authors go beyond a mere descriptive survey by mathematically summarizing the methodologies, facilitating a deeper theoretical understanding.
3. Comprehensive Resource Compilation: They document datasets, evaluation metrics, downstream tasks, and open-source implementations, which serve as an invaluable resource for researchers aiming to develop or benchmark SSL algorithms on graphs.
4. Challenges and Future Directions: The paper outlines several challenges such as scalability, heterogeneity of data, and interpretability. Future directions proposed include exploration of multi-modal graph data, enhanced training efficiency, and improved theoretical underpinnings of graph SSL.

Implications and Future Outlook

The implications of this research are profound in both theoretical and practical spheres. By cataloging and mathematically framing these SSL methods, the paper lays the groundwork for more structured exploration in graph ML models. Practically, reducing the dependency on labeled data in graph-based tasks can significantly cut down operational costs and broaden the applicability of ML systems across various domains such as social network analysis, biological data interpretation, and recommendation systems.

Looking ahead, the field of graph SSL could potentially witness developments in handling complex, large-scale graphs with diverse node and edge types through more advanced models and optimization techniques. The exploration of multi-task and transfer learning paradigms in SSL frameworks might also yield rich dividends in terms of model generalizability and efficiency.

This paper, by encapsulating state-of-the-art techniques, empirical resources, and potential research avenues, serves as a vital reference for ongoing and future research in graph-based self-supervised learning frameworks.