Self-Supervised Graph Learning via Spectral Bootstrapping and Laplacian-Based Augmentations

2025-06-25

AI Summary

Existing self-supervised graph representation learning methods suffer from reliance on negative sampling, manually designed augmentation strategies, and insufficient robustness. To address these issues, this paper proposes LaplaceGNN—a non-contrastive, negative-sampling-free self-supervised Graph Neural Network framework. Its core innovations include: (i) precomputing spectral augmentations based on max-min centrality, explicitly injecting Laplacian matrix signals into node features; and (ii) introducing a spectral-guided adversarial bootstrapping training mechanism that eliminates dependence on handcrafted augmentations and contrastive loss. By unifying spectral graph theory with self-supervised encoding, LaplaceGNN achieves structural-aware representation learning. Extensive experiments on multiple benchmark datasets demonstrate that LaplaceGNN significantly outperforms state-of-the-art self-supervised graph learning methods, while exhibiting linear scalability and enhanced robustness against structural perturbations.

Technology Category

• Machine Learning: Graph-based Machine Learning
• Search and Optimization: Learning to Search
• Computer Vision: Representation Learning for Vision

Application Category

• Graph Algorithms and Modeling for the Web: Graph neural networks and deep learning approaches for Web-related graphs
• Search and Retrieval-Augmented AI: Web learning to rank, online learning, and counterfactual learning for ranking
• Semantics and Knowledge: Methods to enhance, augment, integrate or synergize semantic models such as knowledge graphs and LLMs

Abstract

We present LaplaceGNN, a novel self-supervised graph learning framework that bypasses the need for negative sampling by leveraging spectral bootstrapping techniques. Our method integrates Laplacian-based signals into the learning process, allowing the model to effectively capture rich structural representations without relying on contrastive objectives or handcrafted augmentations. By focusing on positive alignment, LaplaceGNN achieves linear scaling while offering a simpler, more efficient, self-supervised alternative for graph neural networks, applicable across diverse domains. Our contributions are twofold: we precompute spectral augmentations through max-min centrality-guided optimization, enabling rich structural supervision without relying on handcrafted augmentations, then we integrate an adversarial bootstrapped training scheme that further strengthens feature learning and robustness. Our extensive experiments on different benchmark datasets show that LaplaceGNN achieves superior performance compared to state-of-the-art self-supervised graph methods, offering a promising direction for efficiently learning expressive graph representations.

Problem

• Self-supervised graph learning without negative sampling
• Capturing structural representations without contrastive objectives
• Efficient linear scaling for graph neural networks

Innovation

• Self-supervised graph learning without negative sampling
• Laplacian-based augmentations for structural representation
• Adversarial bootstrapped training for robust feature learning