Model-Driven Graph Contrastive Learning (2506.06212v1)

Abstract: We propose $\textbf{MGCL}$, a model-driven graph contrastive learning (GCL) framework that leverages graphons (probabilistic generative models for graphs) to guide contrastive learning by accounting for the data's underlying generative process. GCL has emerged as a powerful self-supervised framework for learning expressive node or graph representations without relying on annotated labels, which are often scarce in real-world data. By contrasting augmented views of graph data, GCL has demonstrated strong performance across various downstream tasks, such as node and graph classification. However, existing methods typically rely on manually designed or heuristic augmentation strategies that are not tailored to the underlying data distribution and operate at the individual graph level, ignoring similarities among graphs generated from the same model. Conversely, in our proposed approach, MGCL first estimates the graphon associated with the observed data and then defines a graphon-informed augmentation process, enabling data-adaptive and principled augmentations. Additionally, for graph-level tasks, MGCL clusters the dataset and estimates a graphon per group, enabling contrastive pairs to reflect shared semantics and structure. Extensive experiments on benchmark datasets demonstrate that MGCL achieves state-of-the-art performance, highlighting the advantages of incorporating generative models into GCL.

• The paper introduces MGCL, a framework that leverages graphon-based augmentations to capture intrinsic graph structures for contrastive learning.
• MGCL replaces arbitrary perturbations with generative models, significantly enhancing node representations and graph classification accuracy.
• Extensive experiments validate that the model-driven approach outperforms traditional methods on multiple benchmark tasks.

Exploring Model-Driven Graph Contrastive Learning

The paper introduces Model-Driven Graph Contrastive Learning (MGCL), an innovative framework designed to enhance the process of graph contrastive learning (GCL) by integrating graphons—probabilistic generative models for graphs. MGCL represents a significant shift from traditional heuristic-based data augmentation methods by leveraging the intrinsic generative structures of graph data.

Background and Motivation

Graph Neural Networks (GNNs) have established efficacy in capturing graph structures, supporting varied applications like bioinformatics and social networks. However, a critical limitation of GNNs remains their reliance on labeled data. Graph Contrastive Learning (GCL) addresses this through a self-supervised approach that does not necessitate labels. GCL traditionally involves maximizing the similarity between different views of a single graph, typically generated through arbitrary or manually defined perturbations. These methods, while useful, often fail to account for the natural probabilistic generative processes underlying graph data, potentially limiting GCL's effectiveness across diverse graph structures.

MGCL Framework

MGCL proposes a refined learning framework where data-driven graph augmentations are informed by graphons. A graphon is a nonparametric function capable of generating random graphs, making it an ideal candidate for understanding and capturing the inherent generative process of the data.

The MGCL framework comprises two primary applications:

1. Node-Level Tasks: The framework begins by estimating the graphon of a single observed graph using the SIGL method, which provides a model of the graphon and generates corresponding latent variables for each node. These latent variables allow the creation of graphon-informed augmentations (GIAs), offering semantically significant graph perturbations that underpin contrastive learning. MGCL leverages these augmentations within a DGI-based contrastive learning framework, enhancing node representation quality by aligning with generative data structures.
2. Graph-Level Tasks: Given a collection of graphs, MGCL employs a model of multiple graphons. The dataset undergoes clustering, assigning graphs to clusters hypothesized to be generated by similar graphons. Each cluster’s graphon is estimated, and graph-level contrastive learning is adjusted to utilize these cluster-specific models, minimizing false negatives and maximizing semantic alignment within the learning process.

Experimental Evaluation and Insights

The methodology's robustness is evidenced by extensive experimental results across standard datasets for both node- and graph-level tasks. MGCL consistently demonstrates superior performance, achieving state-of-the-art results on node classification, node clustering, and graph classification benchmarks. This outperformance over traditional GCL methods underscores the strength of incorporating generative models into the contrastive learning pipeline.

One notable aspect of experimental validation involves assessing the significance of using graphon-driven augmentations over traditional methods like random edge perturbation. Results indicate that graphon-based augmentations lead to a more accurate classification, suggesting improved alignment with the data's intrinsic structures.

Implications and Future Directions

MGCL's approach presents meaningful advancements for graph learning, offering promising pathways for robust semi-supervised and unsupervised learning. The framework harnesses the potential of generative modeling in self-supervised settings, demonstrating that model-driven augmentation can significantly elevate the quality of learned representations, possibly setting a new paradigm for GCL methods.

For future developments, expanding MGCL to incorporate more complex graph generative models and exploring domain-specific applications could provide further enhancements. Additionally, refining the estimation process for multiple graphons and optimizing model parameters will be essential steps in scaling the framework to broader contexts and datasets.

In summary, MGCL represents an astute application of generative modeling principles to graph contrastive learning, promising enhanced adaptability and performance across a spectrum of graph-structured datasets.