arXiv:2605.31373v1 Announce Type: new Abstract: Graph neural networks (GNNs) are limited to modeling pairwise interactions, while higher-order models based on cell complexes achieve greater expressivity but often suffer from poor scalability. We introduce simplified and factored cellular Weisfeiler Leman tests (sCWL and fCWL), which preserve the expressivity of the CWL test while improving computational efficiency. We further introduce the maximal clique complex, enabling scalable CWNs with reduced time and memory complexity while retaining strong empirical performance. To avoid explicit cliqu
This research addresses fundamental scalability limitations in advanced graph neural networks, which are crucial for developing more sophisticated AI models.
Improving the scalability of higher-order graph learning allows for AI to model more complex relationships, potentially leading to breakthroughs in areas requiring nuanced pattern recognition.
The ability to efficiently process higher-order graph structures expands the types of problems amenable to advanced AI techniques, pushing beyond simple pairwise interactions.
- · AI research institutions
- · Companies with complex data challenges
- · Deep learning framework developers
- · SaaS providers leveraging advanced AI
- · AI solutions limited to pairwise interactions
- · Computational hardware without sufficient parallelization
More expressive and computationally efficient graph neural networks become accessible for practical applications.
This could accelerate AI development in domains like drug discovery, material science, and social network analysis.
Advanced AI agents leveraging these scalable graph models might emerge, capable of more sophisticated reasoning over interconnected information.
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Read at arXiv cs.LG