Self-Supervised Graph Learning via Spectral Bootstrapping and Laplacian-Based Augmentations
This addresses the need for efficient and robust self-supervised learning in graph neural networks, applicable across diverse domains, though it appears incremental by building on existing spectral and bootstrapping techniques.
The paper tackles the problem of self-supervised graph learning by introducing LaplaceGNN, a framework that eliminates negative sampling and handcrafted augmentations, achieving superior performance on benchmark datasets compared to state-of-the-art methods.
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.