Towards Foundation Models on Graphs: An Analysis on Cross-Dataset Transfer of Pretrained GNNs (2412.17609v1)

Abstract: To develop a preliminary understanding towards Graph Foundation Models, we study the extent to which pretrained Graph Neural Networks can be applied across datasets, an effort requiring to be agnostic to dataset-specific features and their encodings. We build upon a purely structural pretraining approach and propose an extension to capture feature information while still being feature-agnostic. We evaluate pretrained models on downstream tasks for varying amounts of training samples and choices of pretraining datasets. Our preliminary results indicate that embeddings from pretrained models improve generalization only with enough downstream data points and in a degree which depends on the quantity and properties of pretraining data. Feature information can lead to improvements, but currently requires some similarities between pretraining and downstream feature spaces.

• The paper analyzes the feasibility and dynamics of transferring pretrained Graph Neural Networks (GNNs) across diverse datasets to explore Graph Foundation Models.
• It introduces a feature-structuralization strategy and tests GNN transfer across datasets like ZINC-12k, ogbg-molpcba, and peptides-func.
• Findings indicate transfer effectiveness depends on sufficient in-domain data, feature structuralization has inconsistent cross-dataset benefits, and mixed-dataset pretraining can improve generalization.

Analysis of Cross-Dataset Transfer of Pretrained Graph Neural Networks

The paper "Towards Foundation Models on Graphs: An Analysis on Cross-Dataset Transfer of Pretrained GNNs" offers a rigorous examination of the feasibility and dynamics of deploying pretrained Graph Neural Networks (GNNs) across diverse datasets. Targeting the ambition of establishing Graph Foundation Models (GFMs), this paper explores the critical question of transferring learning across graph-based datasets, where variations in data structure and feature semantics can introduce significant challenges.

Core Contributions

The investigation centers around the hypothesis that pretrained GNNs can generalize effectively across datasets if they possess a degree of feature-agnosticism. This premise is tested through a novel pretraining methodology designed to harness structural encodings while remaining indifferent to specific dataset features.

Key aspects include:

1. Structuralization Methodology: The authors introduce a feature-structuralization strategy, which adapts dataset-specific feature information into structural graph forms. This transformation is hypothesized to leverage graph structure for universal representations while avoiding explicit feature dependency.
2. Pretraining Corpus: Experiments exploit a wide array of graph datasets, specifically: ZINC-12k, ogbg-molpcba, and peptides-func -- each featuring distinct structural and feature attributes. This diversity tests the robustness of the proposed methodology across differing data distributions.
3. Performance Metrics: Effectiveness is gauged by observing performance variations on tasks when models are pretrained on various subsets or combinations of the available datasets.

Observations and Results

The paper's findings underscore several nuanced aspects of cross-dataset transfer:

• Effectiveness Conditional on Dataset Size: Results indicate tangible gains in downstream task performance when sufficient in-domain data accompanies the pretrained model, underscoring a data dependency aspect crucial to the transfer's success.
• Feature Structuralization Impact: The experiments demonstrate inconsistent benefits from structuralized pretraining in contrast to vanilla GNN pretraining. Although structuralization supports robust in-domain performance gains, its advantage diminishes across datasets with disparate feature spaces.
• Dataset Synergy: Mixed dataset pretraining, involving differing but related datasets, often results in better generalization when transferring to new domains. This observation highlights the importance of strategic dataset selection during the pretraining phase.

Implications and Future Directions

This work is pivotal in shaping the understanding of transferrable graph representations in AI. Practically, the notion of foundation models for graphs heralds potential leaps in multi-domain applications, akin to foundation models in text and vision. The results suggest that advancing pretraining techniques with an improved understanding of feature representation and diversity can yield more universally applicable GNNs.

Theoretically, this paper opens numerous research paths:

• Exploration into more nuanced architectural adaptations that can harmonize feature-agnosticism with feature-expressivity.
• Larger-scale experiments to ascertain the ceiling of transfer capabilities within even broader dataset pools.
• Development of standardized benchmarks and methodologies for evaluating cross-dataset transfer capabilities in graph contexts.

In conclusion, while the path to robust Graph Foundation Models requires overcoming several hurdles, including the reconciliation of feature representational differences across domains, this paper lays an essential foundation. Further exploration inspired by these findings is likely to catalyze progress, potentially transforming how we utilize GNNs for myriad applications spreading across diverse data landscapes.