Augmentation-Free Graph Contrastive Learning of Invariant-Discriminative Representations (2210.08345v2)

Abstract: The pretasks are mainly built on mutual information estimation, which requires data augmentation to construct positive samples with similar semantics to learn invariant signals and negative samples with dissimilar semantics in order to empower representation discriminability. However, an appropriate data augmentation configuration depends heavily on lots of empirical trials such as choosing the compositions of data augmentation techniques and the corresponding hyperparameter settings. We propose an augmentation-free graph contrastive learning method, invariant-discriminative graph contrastive learning (iGCL), that does not intrinsically require negative samples. iGCL designs the invariant-discriminative loss (ID loss) to learn invariant and discriminative representations. On the one hand, ID loss learns invariant signals by directly minimizing the mean square error between the target samples and positive samples in the representation space. On the other hand, ID loss ensures that the representations are discriminative by an orthonormal constraint forcing the different dimensions of representations to be independent of each other. This prevents representations from collapsing to a point or subspace. Our theoretical analysis explains the effectiveness of ID loss from the perspectives of the redundancy reduction criterion, canonical correlation analysis, and information bottleneck principle. The experimental results demonstrate that iGCL outperforms all baselines on 5 node classification benchmark datasets. iGCL also shows superior performance for different label ratios and is capable of resisting graph attacks, which indicates that iGCL has excellent generalization and robustness. The source code is available at https://github.com/lehaifeng/T-GCN/tree/master/iGCL.

• The paper introduces an augmentation-free approach that eliminates resource-intensive data augmentation by utilizing a Siamese network and local structural information for positive sample construction.
• The paper presents an invariant-discriminative loss that combines MSE and orthonormal constraints to balance invariant and discriminative features, mitigating collapsing issues.
• Empirical results on five benchmark datasets demonstrate that iGCL outperforms traditional methods, ensuring robustness in low-label and adversarial scenarios.

Overview of "Augmentation-Free Graph Contrastive Learning of Invariant-Discriminative Representations"

The paper "Augmentation-Free Graph Contrastive Learning of Invariant-Discriminative Representations" by Haifeng Li et al. introduces an innovative method in the domain of graph contrastive learning (GCL), aiming to overcome the challenges associated with empirical data augmentation and the dependency on negative samples which are typical in traditional contrastive learning approaches.

Core Contributions

The proposed method, termed invariant-discriminative graph contrastive learning (iGCL), offers significant advancements with a strategic focus on learning invariant and discriminative representations without the reliance on cumbersome and computationally expensive data augmentation practices. The key contributions of this paper can be summarized as follows:

1. Augmentation-Free Approach: Unlike conventional GCL methods that rely heavily on data augmentation to construct positive and negative samples, iGCL leverages the Siamese network architecture to derive positive samples directly in the representation space. This novel perspective eliminates the resource-intensive trial-and-error process involved in choosing suitable data augmentation techniques.
2. Positive Sample Construction Strategy: The paper introduces a method to utilize local structural information by identifying the most similar representations from neighboring nodes. This approach not only increases the diversity of positive samples but also enhances the quality by integrating the representational similarity effectively.
3. Invariant-Discriminative Loss (ID Loss): The ID loss is crafted to ensure that learned representations achieve a balance between invariance and discriminability. It comprises two components: an invariance term based on the mean square error (MSE), and a discrimination term using an orthonormal constraint. The latter ensures that different dimensions of the representations remain independent, thereby preventing collapsing issues typical in conventional setups.
4. Theoretical Insights: The authors provide a robust theoretical foundation for ID loss, illustrating its connections with the redundancy reduction criterion, canonical correlation analysis, and the information bottleneck principle. This theoretical exposition underscores the method's capacity for effective representation learning.

Empirical Evaluation

Empirically, iGCL's performance is demonstrated on five node classification benchmark datasets, where it outperforms all baseline models, including both supervised and other contrastive learning approaches. Notably, the method shows strong results across various label ratios, highlighting its adaptability and potential in scenarios with limited label availability. The robustness of iGCL is further validated against graph attacks, indicating its resilience in preserving performance under adversarial conditions.

Implications and Future Directions

The implications of iGCL are multifold:

• Theoretical Significance: By eliminating the need for negative samples and utilizing feature-level mutual information estimation, iGCL challenges traditional paradigms within GCL and opens new avenues for contrastive learning research.
• Practical Utility: The augmentation-free nature of iGCL reduces the computational burden and enhances the scalability of graph-based learning models, thereby offering practical benefits for real-world applications involving large-scale graphs.
• Future Research: The potential for iGCL to be extended or adapted to other domains or variations of GCL remains an interesting avenue. Moreover, exploring enhancements to the network architecture and optimizing the orthonormal constraint could further improve its robustness and accuracy.

In conclusion, this paper presents a compelling advancement in graph contrastive learning, offering a method that is both efficient and effective for learning invariant-discriminative representations without augmentation or negative sampling dependencies. The insights provided may significantly influence future developments and applications in the field of AI and graph-based learning models.