Towards Fast Graph Generation via Autoregressive Noisy Filtration Modeling

TMLR Paper5692 Authors

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. This approach generalizes prior autoregressive methods by enabling the construction of non-induced 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.

Submission Length: Regular submission (no more than 12 pages of main content)

Assigned Action Editor: ~Alessandro_Sperduti1

Submission Number: 5692