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[2502.02415] Fast Graph Generation via Autoregressive Noisy Filtration Modeling
Summary
This paper presents Autoregressive Noisy Filtration Modeling (ANFM), a new framework for fast graph generation that balances quality and speed, outperforming existing models.
Why It Matters
The ability to generate high-quality graphs quickly is crucial in various applications, including network analysis and machine learning. ANFM addresses the trade-off between generation speed and sample quality, making it a significant advancement in the field of graph generation.
Key Takeaways
- ANFM improves graph generation speed by over 100 times compared to existing models.
- The framework utilizes noise augmentation and reinforcement learning to mitigate exposure bias.
- ANFM can model both edge addition and deletion, enhancing its flexibility.
- The method matches state-of-the-art diffusion models in quality.
- Publicly available source code facilitates further research and application.
Computer Science > Machine Learning arXiv:2502.02415 (cs) [Submitted on 4 Feb 2025 (v1), last revised 15 Feb 2026 (this version, v2)] Title:Fast Graph Generation via Autoregressive Noisy Filtration Modeling Authors:Markus Krimmel, Jenna Wiens, Karsten Borgwardt, Dexiong Chen View a PDF of the paper titled Fast Graph Generation via Autoregressive Noisy Filtration Modeling, by Markus Krimmel and 3 other authors View PDF HTML (experimental) Abstract:Existing graph generative models often face a critical trade-off between sample quality and generation speed. We introduce Autoregressive Noisy Filtration Modeling (ANFM), a flexible autoregressive framework that addresses both challenges. ANFM leverages filtration, a concept from topological data analysis, to transform graphs into short sequences of subgraphs. We identify exposure bias as a potential hurdle in autoregressive graph generation and propose noise augmentation and reinforcement learning as effective mitigation strategies, which allow ANFM to learn both edge addition and deletion operations. This unique capability enables ANFM to correct errors during generation by modeling non-monotonic graph sequences. Our results show that ANFM matches state-of-the-art diffusion models in quality while offering over 100 times faster inference, making it a promising approach for high-throughput graph generation. The source code is publicly available at this https URL . Subjects: Machine Learning (cs.LG) Cite as: arXiv:2502.02415 [cs....
