A Unified Perspective for Learning Graph Representations Across Multi-Level Abstractions

2026-05-12

AI Summary

Existing graph self-supervised learning methods are typically confined to a single level of abstraction and employ uniform penalty strengths, limiting their ability to flexibly integrate multi-granularity structural information. This work proposes a unified multi-level contrastive learning framework that simultaneously models representations at the node, neighborhood, cluster, and graph levels, optimizing them through a linear combination of similarity and dissimilarity scores. Additionally, a parameter-free, fine-grained self-weighting mechanism is introduced to dynamically adjust sample weights during training, enhancing optimization efficiency. To the best of our knowledge, this is the first approach to achieve unified modeling of multi-level graph representations, consistently outperforming state-of-the-art methods across node classification, clustering, and link prediction tasks on multiple real-world datasets.

Abstract

Graph Self-Supervised Learning (GSSL) has emerged as a powerful paradigm for generating high-quality representations for graph-structured data. While multi-scale graph contrastive learning has received increasing attention, many existing methods still predominantly focus on a single graph abstraction level. To address this limitation, we propose a unified contrastive framework that can target node-level, proximity-level, cluster-level, and graph-level information and integrate them through a linear combination of similarity scores on positive pairs and dissimilarity scores (i.e., similarity scores on negative pairs). Furthermore, current approaches typically assign uniform penalty strengths to all examples, which reduces optimization flexibility and leads to ambiguous convergence status. To overcome this, we introduce a novel parameter-free fine-grained self-weighting mechanism that adaptively assigns weights to individual similarity and dissimilarity scores. The proposed mechanism emphasizes the scores that deviate significantly from their target values. Our approach not only enhances optimization flexibility but also eliminates the computational overhead of hyperparameter tuning in conventional multi-task GSSL methods. Comprehensive experiments on real-world datasets show that our methods consistently outperform state-of-the-art approaches across downstream tasks, including classification, clustering, and link prediction, in both single-level and multi-level scenarios.

Problem

• Graph Self-Supervised Learning
• Multi-Level Abstraction
• Contrastive Learning
• Optimization Flexibility
• Graph Representation

Innovation

• Graph Self-Supervised Learning
• Multi-Level Abstraction
• Contrastive Learning
• Self-Weighting Mechanism
• Unified Framework