SimGRACE: A Simple Framework for Graph Contrastive Learning without Data Augmentation (2202.03104v3)

Abstract: Graph contrastive learning (GCL) has emerged as a dominant technique for graph representation learning which maximizes the mutual information between paired graph augmentations that share the same semantics. Unfortunately, it is difficult to preserve semantics well during augmentations in view of the diverse nature of graph data. Currently, data augmentations in GCL that are designed to preserve semantics broadly fall into three unsatisfactory ways. First, the augmentations can be manually picked per dataset by trial-and-errors. Second, the augmentations can be selected via cumbersome search. Third, the augmentations can be obtained by introducing expensive domain-specific knowledge as guidance. All of these limit the efficiency and more general applicability of existing GCL methods. To circumvent these crucial issues, we propose a \underline{Sim}ple framework for \underline{GRA}ph \underline{C}ontrastive l\underline{E}arning, \textbf{SimGRACE} for brevity, which does not require data augmentations. Specifically, we take original graph as input and GNN model with its perturbed version as two encoders to obtain two correlated views for contrast. SimGRACE is inspired by the observation that graph data can preserve their semantics well during encoder perturbations while not requiring manual trial-and-errors, cumbersome search or expensive domain knowledge for augmentations selection. Also, we explain why SimGRACE can succeed. Furthermore, we devise adversarial training scheme, dubbed \textbf{AT-SimGRACE}, to enhance the robustness of graph contrastive learning and theoretically explain the reasons. Albeit simple, we show that SimGRACE can yield competitive or better performance compared with state-of-the-art methods in terms of generalizability, transferability and robustness, while enjoying unprecedented degree of flexibility and efficiency.

• The paper demonstrates that encoder perturbation replaces traditional data augmentation in GCL, preserving graph semantics and reducing computational overhead.
• SimGRACE achieves competitive alignment and uniformity metrics, outperforming methods like GraphCL in efficiency and robustness.
• The framework introduces adversarial training to create flatter loss landscapes, ensuring robust and transferable graph representations.

SimGRACE: A Framework for Graph Contrastive Learning without Data Augmentation

The paper presents SimGRACE, a novel framework for Graph Contrastive Learning (GCL) that circumvents the challenges associated with data augmentations. Traditional GCL relies heavily on data augmentations, which can be inefficient, domain-specific, and computationally expensive due to the requirement of manual tuning, cumbersome search, or domain expertise. In contrast, SimGRACE eliminates these limitations by leveraging encoder perturbations rather than data augmentations to generate correlated views for contrast.

Methodology Overview

SimGRACE utilizes the original graph data as input and employs a Graph Neural Network (GNN) alongside its perturbed version. Here, the perturbation is not applied to the data itself but to the encoder, creating two correlated views from the same input graph. Encoder perturbation is achieved by adding Gaussian noise to the model weights, thus crafting 'positive pairs’ for contrastive learning. The use of encoder perturbation in lieu of traditional data augmentation is inspired by insights that this approach preserves the semantic integrity of graph data while reducing dependency on manual tweaks or domain-specific decisions.

The SimGRACE framework advances the field by offering a method that preserves graph semantics and enhances flexibility and efficiency. Theoretical justification for this approach is provided using alignment and uniformity metrics, showing that SimGRACE achieves significant gains in alignment with competitive uniformity metrics when compared to traditional methods like GraphCL and MoCL.

Key Contributions

1. Framework Elegance: SimGRACE provides a simple yet efficient GCL framework, eliminating the need for augmentation-specific configurations. Notably, it achieves comparable or superior performance in generalizability, transferability, and robustness against state-of-the-art methods, as demonstrated through extensive empirical evaluations.
2. Adversarial Robustness: The paper introduces AT-SimGRACE, an adversarial training scheme to fortify SimGRACE against adversarial attacks. This is achieved by perturbing the encoder adversarially which, according to theoretical analyses, results in a flatter loss landscape, thereby enhancing the robustness of graph representations obtained via contrastive learning.
3. Experimental Validation: The proposed methods were evaluated on a wide array of datasets, including both social and biochemical graphs. Furthermore, the experiments showcase the superior efficiency of SimGRACE in terms of reduced training time and memory consumption when benchmarked against competitors like GraphCL and JOAOv2.

Implications and Future Directions

SimGRACE's advancement in GCL sets a precedent for designing simpler, more efficient models in graph-based learning domains. By doing away with traditional data augmentations, SimGRACE not only reduces computational burden but also broadens applicability across domains like social networks and biochemical graph tasks. The robustness improvements through adversarial training further ensure the resilience of learned representations, crucial for real-world applications where data integrity can often be compromised.

Looking forward, research could be directed towards exploring the application of encoder perturbation techniques beyond graph domains, potentially enhancing contrastive learning techniques in computer vision and NLP. The paper also opens a pathway for embedding similar strategies in pre-trained GNN models for tasks such as social analysis and biochemical property prediction, benefiting from their efficiency and adaptability.

In summary, SimGRACE marks a significant step in refining graph contrastive learning methodologies, offering a compelling, augmentation-free alternative that balances simplicity with high performance. This work is poised to influence future research directions, particularly in developing generalizable, robust GNN frameworks across diverse application domains.