arXiv:2506.01467v3 Announce Type: replace Abstract: Graph generative models perform well on small structured data but struggle to scale to large, complex structures. Hierarchical approaches improve scalability but often ignore node and edge features, which are critical in real-world applications, particularly for hypergraphs that model higher-order relationships. In this paper, we propose FAHNES (feature-aware (hyper)graph generation via next-scale prediction), a hierarchical framework that jointly generates topology and features for graphs and hypergraphs. FAHNES builds multi-scale representa
The increasing complexity and scale of real-world data necessitate more sophisticated graph generative models capable of handling features and hierarchical structures.
Advanced graph generative models are crucial for developing more effective AI systems that can understand and create complex, high-dimensional data, pushing the boundaries of AI capabilities.
This research introduces a novel hierarchical framework that allows AI to jointly generate both the topology and features of complex graphs and hypergraphs, improving scalability and fidelity.
- · AI/ML researchers
- · Graph AI startups
- · Healthcare/biotech (drug discovery)
- · Social network analysis platforms
- · AI models reliant on simplistic data structures
Improved performance and scalability of AI models in domains requiring complex relational data understanding.
Acceleration of research in areas like materials science, drug discovery, and cybersecurity by enabling more effective simulation and generation of complex networks.
New classes of AI agents able to autonomously design and optimize complex systems, impacting engineering and scientific discovery.
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Read at arXiv cs.LG