Rethinking and Scaling Up Graph Contrastive Learning: An Extremely Efficient Approach with Group Discrimination (2206.01535v2)

Abstract: Graph contrastive learning (GCL) alleviates the heavy reliance on label information for graph representation learning (GRL) via self-supervised learning schemes. The core idea is to learn by maximising mutual information for similar instances, which requires similarity computation between two node instances. However, GCL is inefficient in both time and memory consumption. In addition, GCL normally requires a large number of training epochs to be well-trained on large-scale datasets. Inspired by an observation of a technical defect (i.e., inappropriate usage of Sigmoid function) commonly used in two representative GCL works, DGI and MVGRL, we revisit GCL and introduce a new learning paradigm for self-supervised graph representation learning, namely, Group Discrimination (GD), and propose a novel GD-based method called Graph Group Discrimination (GGD). Instead of similarity computation, GGD directly discriminates two groups of node samples with a very simple binary cross-entropy loss. In addition, GGD requires much fewer training epochs to obtain competitive performance compared with GCL methods on large-scale datasets. These two advantages endow GGD with very efficient property. Extensive experiments show that GGD outperforms state-of-the-art self-supervised methods on eight datasets. In particular, GGD can be trained in 0.18 seconds (6.44 seconds including data preprocessing) on ogbn-arxiv, which is orders of magnitude (10,000+) faster than GCL baselines while consuming much less memory. Trained with 9 hours on ogbn-papers100M with billion edges, GGD outperforms its GCL counterparts in both accuracy and efficiency.

An Analysis of "Rethinking and Scaling Up Graph Contrastive Learning: An Extremely Efficient Approach with Group Discrimination"

The paper "Rethinking and Scaling Up Graph Contrastive Learning: An Extremely Efficient Approach with Group Discrimination" addresses the inefficiencies associated with traditional Graph Contrastive Learning (GCL) methods in the context of graph representation learning. Graph Neural Networks (GNNs) have gained prominence for their ability to effectively handle graph-structured data, but they often rely on supervised learning, which demands substantial labeled data. GCL methods attempt to alleviate this reliance by employing self-supervised schemes, focusing on mutual information maximization between node instances. However, these methods entail considerable computational and memory resources.

Core Contributions

1. Introduction of Group Discrimination (GD): The paper identifies a flaw in existing GCL approaches, specifically in DGI and MVGRL, concerning the inappropriate use of the Sigmoid function. This insight leads to the proposal of a novel learning paradigm named Group Discrimination (GD). Rather than measuring similarity between node pairs, GD emphasizes the discrimination between groups of node samples, which streamlines the process significantly using a simplistic binary cross-entropy loss.
2. Proposition of Graph Group Discrimination (GGD): Building upon GD, the authors introduce GGD, a method intended for efficient self-supervised graph representation learning. GGD eliminates the need for elaborate similarity computations and reduces training epochs, thus significantly improving the training efficiency and scalability of models.
3. Empirical Validation: Through comprehensive experimentation on eight datasets, GGD demonstrated superior performance compared to state-of-the-art self-supervised methods. Notably, GGD can achieve model training in 0.18 seconds on the ogbn-arxiv dataset, which is approximately 10,000 times faster than some GCL baselines, highlighting its advantage in both speed and memory consumption.

Numerical Results and Observations

• The efficiency of GGD is evident in its ability to match or exceed the performance of existing GCL models across multiple datasets with significantly fewer resources. In particular, on the ogbn-papers100M with over 1 billion edges, GGD outdoes its counterparts in both accuracy and resource efficiency within only nine hours of training.
• The paper's exploration of node embeddings through aggregation rather than direct pairwise comparisons leads to a noticeable reduction in computational overhead without compromising the representational quality.
• The results challenge the conventional methodology of infusing additional network complexity to enhance learning outcomes, which typically would result in greater computational expense and diminished scalability.

Implications and Future Directions

The introduction of GD and GGD implies a potential paradigm shift in graph representation learning, where efficiency does not need to be sacrificed for accuracy. This approach could redefine best practices in developing GNNs for real-world applications, especially those constrained by computational capacity.

The removal of reliance on mutual information maximization highlights an opportunity to rethink other self-supervised methodologies that could benefit from a similar reconsideration of core processes. Moreover, the concept of group discrimination may inspire analogous frameworks across different domains of contrastive learning.

Speculations on Future Directions

This research opens the avenue for exploring multi-group discrimination frameworks, where more than two contrasting groups are employed to possibly unveil deeper structural insights within data. Furthermore, enhancing augmentation techniques used within GGD could amplify the discriminatory power and robustness of the group discrimination approach.

In conclusion, the paper contributes a pivotal perspective in graph representation learning, challenging pre-existing assumptions and laying groundwork for more resource-efficient methodologies. Future advances in this field could significantly benefit from the mechanisms and results introduced, potentially widening the applicability of GNNs across various data-intensive domains. The bridging of theoretical analysis with substantial empirical support renders this work both impactful and a critical reference for subsequent exploration in graph-based artificial intelligence.