Summary of Random Walk Diffusion For Efficient Large-scale Graph Generation, by Tobias Bernecker et al.
Random Walk Diffusion for Efficient Large-Scale Graph Generation
by Tobias Bernecker, Ghalia Rehawi, Francesco Paolo Casale, Janine Knauer-Arloth, Annalisa Marsico
First submitted to arxiv on: 8 Aug 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Social and Information Networks (cs.SI)
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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 proposed ARROW-Diff method for large-scale graph generation employs a novel random walk-based diffusion approach, combining two components in an iterative process. This approach outperforms existing baseline methods in terms of both generation time and multiple graph statistics, indicating the high quality of the generated graphs. The model utilizes auto-regressive and random walk techniques to efficiently generate new graphs with a data distribution similar to real-world graphs. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary ARROW-Diff is a new way to create big networks that are similar to real ones. It uses a special kind of walking on graphs and then prunes the results to make sure they’re good. This method is fast and makes high-quality networks, beating other methods in both speed and quality. |
Keywords
» Artificial intelligence » Diffusion
