Layer-wise contrastive network for unsupervised graph representation learning

Unsupervised graph representation learning has emerged as a cornerstone for extracting meaningful insights from complex relational data. While contrastive learning paradigms have achieved notable success, they traditionally rely on stochastic data augmentations to generate multiple input views—a process that often incurs significant computational overhead and sensitivity to augmentation quality. To transcend these limitations, we propose the Layer-wise Contrastive Network (LCN), a novel and efficient paradigm that redefines the construction of contrastive views. Unlike conventional methods that rely on extrinsic data perturbations, LCN exploits the intrinsic architectural hierarchy of Graph Convolutional Networks. By treating distinct neural layers as different views of the same graph instance, we introduce a contrastive objective that enforces consistency between shallow and deep representations. This mechanism not only eliminates the need for expensive augmentation operations but also distills and preserves fundamental node characteristics from the original graph throughout deeper layers. Furthermore, LCN serves as a flexible plug-and-play framework, exemplified by its extension into Wide LCN, which integrates with traditional augmentation-based methods. Extensive evaluations across both transductive and inductive benchmarks demonstrate that our method achieves superior representational robustness and computational efficiency, offering a scalable and principled perspective for future graph-based contrastive learning. Our code is available at https://github.com/XiangluZhu/LCN.git . • We introduce a novel contrastive loss to learn graph representations by contrasting shallow and deep features. • Our method is flexible and can be readily combined with existing graph contrastive learning techniques that utilize data augmentation. • We demonstrate outstanding performance with our method across four benchmark datasets for node classification.