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Position: Graph Foundation Models are Already Here (2402.02216v3)

Published 3 Feb 2024 in cs.LG

Abstract: Graph Foundation Models (GFMs) are emerging as a significant research topic in the graph domain, aiming to develop graph models trained on extensive and diverse data to enhance their applicability across various tasks and domains. Developing GFMs presents unique challenges over traditional Graph Neural Networks (GNNs), which are typically trained from scratch for specific tasks on particular datasets. The primary challenge in constructing GFMs lies in effectively leveraging vast and diverse graph data to achieve positive transfer. Drawing inspiration from existing foundation models in the CV and NLP domains, we propose a novel perspective for the GFM development by advocating for a ``graph vocabulary'', in which the basic transferable units underlying graphs encode the invariance on graphs. We ground the graph vocabulary construction from essential aspects including network analysis, expressiveness, and stability. Such a vocabulary perspective can potentially advance the future GFM design in line with the neural scaling laws. All relevant resources with GFM design can be found here.

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Citations (14)
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Summary

  • The paper introduces a framework for Graph Foundation Models via a novel graph vocabulary that captures invariant structural properties across graphs.
  • It emphasizes transferability principles balancing expressiveness and stability to achieve robust cross-domain performance.
  • The categorization into task-specific, domain-specific, and prototype models outlines clear pathways for scalable and versatile applications.

Insights on Graph Foundation Models

The paper "Graph Foundation Models" provides a comprehensive exploration into the emerging field of Graph Foundation Models (GFMs), a burgeoning area of research focused on developing graph models with the ability to generalize across different graphs and tasks. The primary challenge identified in constructing such models is achieving positive transfer across graphs with varying structural paradigms.

Core Contributions

The authors propose a foundational framework for GFMs by advocating the creation of a "graph vocabulary". This vocabulary consists of basic transferable units that encapsulate essential invariances on graphs, drawing inspiration from successful foundation models in Computer Vision (CV) and NLP. By constructing a robust graph vocabulary grounded in network analysis, theoretical foundations, and stability considerations, the authors aim to enhance the design of future GFMs, analogous to leveraging neural scaling laws.

Challenges and Direction

Despite notable advances in foundation models within other domains, GFMs remain nascent, constrained by the complexity of diverse graph structures. Existing GFMs have showcased potential in niche domains—such as knowledge graphs and molecular structures—but often lack versatility.

  1. Graph Vocabulary: The concept of a graph vocabulary is central to the paper, where the vocabulary must effectively encode invariant structures across different graph datasets and tasks. This pursuit aligns with efforts in NLP and CV, where vocabularies form the building blocks for understanding complex data through simpler, discrete units.
  2. Transferability Principles: Establishing a unified set of transferability principles could drive the development of GFMs. These principles, rooted in both expressiveness and stability, aim to ensure robust cross-domain transferability. The authors highlight expressiveness—where representations must distinguish between different graph structures—and stability, representing resilience to graph perturbations.
  3. Model Categorization: The paper categorizes existing GFMs into task-specific, domain-specific, and prototype models, each defined by their transferability scope. ULTRA and DiG are cited as models achieving successful task and domain adjacency, respectively.

Numerical Results and Implications

The paper does not emphasize specific numerical results but rather focuses on theoretical frameworks and principles necessary for the advancement of GFMs. However, the implications are far-reaching, suggesting that a well-constructed GFM could drastically reduce the need for bespoke model solutions for each graph-related problem and task.

Speculation on Future Developments

  1. Scaling Laws: Mirroring achievements in NLP and CV, the authors speculate on the potential application of neural scaling laws—stating that increasing model and data size correlates with performance gains—in the context of graphs.
  2. Broader Applications: While the current focus of GFMs is on conventional graph tasks like node classification, link prediction, and graph classification, the framework proposed could extend into other domains, such as scene graphs for CV and physical networks for scientific research.

Conclusion

The pathway to a true Graph Foundation Model is challenged by the variety and complexity inherent in graph structures. Successful realization could revolutionize graph analytics, enabling a generalized approach adaptable to varied tasks and datasets. This paper lays the groundwork for ongoing research, signifying a pivotal shift towards graph models with broad, versatile applicability, driven by a foundational understanding of graph vocabulary and principles.

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