Summary of Pard: Permutation-invariant Autoregressive Diffusion For Graph Generation, by Lingxiao Zhao et al.
Pard: Permutation-Invariant Autoregressive Diffusion for Graph Generation
by Lingxiao Zhao, Xueying Ding, Leman Akoglu
First submitted to arxiv on: 6 Feb 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Machine Learning (stat.ML)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary The paper introduces a novel graph generation model called Permutation-invariant Auto Regressive Diffusion (PARD) that combines the strengths of autoregressive and diffusion models. PARD generates graphs in a block-by-block, autoregressive fashion, leveraging a shared diffusion model with an equivariant network to conditionally model each block’s probability. The authors also propose a higher-order graph transformer that integrates transformer with PPGN, allowing for parallel training of all blocks. Without extra features, PARD achieves state-of-the-art performance on molecular and non-molecular datasets, including the large MOSES dataset containing 1.9M molecules. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary PARD is a new way to generate graphs that uses ideas from both autoregressive models and diffusion models. These two types of models are good at different things, but PARD combines their strengths to make it better at generating graphs. The model works by breaking down the graph into smaller blocks and then using a shared diffusion model to decide what each block should look like. This approach is more efficient than previous methods and allows PARD to generate large graphs quickly. The results show that PARD is very good at generating graphs, especially for molecular and non-molecular data. |
Keywords
* Artificial intelligence * Autoregressive * Diffusion * Diffusion model * Probability * Transformer
