Bringing Your Own View: Graph Contrastive Learning without Prefabricated Data Augmentations (2201.01702v1)

Abstract: Self-supervision is recently surging at its new frontier of graph learning. It facilitates graph representations beneficial to downstream tasks; but its success could hinge on domain knowledge for handcraft or the often expensive trials and errors. Even its state-of-the-art representative, graph contrastive learning (GraphCL), is not completely free of those needs as GraphCL uses a prefabricated prior reflected by the ad-hoc manual selection of graph data augmentations. Our work aims at advancing GraphCL by answering the following questions: How to represent the space of graph augmented views? What principle can be relied upon to learn a prior in that space? And what framework can be constructed to learn the prior in tandem with contrastive learning? Accordingly, we have extended the prefabricated discrete prior in the augmentation set, to a learnable continuous prior in the parameter space of graph generators, assuming that graph priors per se, similar to the concept of image manifolds, can be learned by data generation. Furthermore, to form contrastive views without collapsing to trivial solutions due to the prior learnability, we have leveraged both principles of information minimization (InfoMin) and information bottleneck (InfoBN) to regularize the learned priors. Eventually, contrastive learning, InfoMin, and InfoBN are incorporated organically into one framework of bi-level optimization. Our principled and automated approach has proven to be competitive against the state-of-the-art graph self-supervision methods, including GraphCL, on benchmarks of small graphs; and shown even better generalizability on large-scale graphs, without resorting to human expertise or downstream validation. Our code is publicly released at https://github.com/Shen-Lab/GraphCL_Automated.

Essay: Advancements in Graph Contrastive Learning with Learnable Priors

The paper "Bringing Your Own View: Graph Contrastive Learning without Prefabricated Data Augmentations" introduces an innovative approach to graph contrastive learning, which circumvents the use of prefabricated data augmentations. The authors address the limitations of existing graph self-supervised learning frameworks, particularly the dependency on domain-expert driven augmentations, such as those required in GraphCL. They propose a novel methodology whereby the prior knowledge for data augmentation is learned autonomously during the model training phase through the use of graph generative models.

Key Contributions and Methodology

This research pivots on three critical questions: how to define the space of graph augmented views, the principles that govern learning a prior in this space, and the framework required for training. The primary contributions of this paper include:

1. Transition to a Learnable Continuous Prior: The authors propose utilizing graph generative models, simulated by neural networks, to replace the traditional discrete, hand-tuned augmentations with learnable continuous priors. Leveraging techniques from variational graph autoencoders (VGAE) ensures that the prior can be dynamically adjusted during training.
2. Principled Regularization Methodologies: To offset the potential collapse onto trivial solutions, the authors apply the principles of Information Minimization (InfoMin) and Information Bottleneck (InfoBN). These principles guide the learning process by ensuring that the model retains meaningful information that contributes to successful task execution.
3. Framework Formulation and Implementation: A bi-level optimization framework is developed to embed these principles into the graph contrastive learning model. This setup ensures that the learning of priors is integrated seamlessly into the contrastive learning process, enhancing the adaptability of the model to various datasets and conditions.

Performance and Implications

The empirical results demonstrate that the proposed learnable priors match or exceed the performance of state-of-the-art methods, particularly for large-scale, heterogeneous graphs. On benchmarks like ogbg-ppa and ogbg-code, models employing learned priors showed notable improvements over GraphCL, which relies on manual augmentations. The scalability and generalization capabilities of learnable priors highlight their potential for broad applications across domains. This transition reduces reliance on downstream validation or domain-specific expertise and offers a more universally applicable solution to graph data challenges.

One implication is that this learnable prior framework could extend beyond graphs to other domains that suffer from similar data augmentation challenges, especially where domain knowledge is scarce or dataset-specific priors are challenging to determine. Future developments may explore integrating more sophisticated graph generation techniques, potential integration with more robust generative adversarial networks, or even hybrid models that incorporate some degree of domain knowledge as a starting point.

Future Directions

The shift from manually curated to autonomously learned augmentations opens new avenues for research. Further exploration into the types and breadth of generative models could optimize the learning of priors and possibly redefine augmentation strategies for other machine learning domains. Additionally, investigating alternatives to the InfoMin and InfoBN principles, or refining these principles, might yield even more robust performance. Moreover, combining learned priors with reinforcement learning strategies might provide a potential conduit to not only adaptively learn augmentations but also dynamically adjust to changes in real-time data streams.

In conclusion, this paper's novel approach provides a flexible, efficient, and potentially transformative method for graph contrastive learning. It foregoes the cumbersome reliance on domain-specific augmentations, paving the way for more autonomous, adaptable machine learning paradigms in graph analysis and beyond.