Random Walk Diffusion for Efficient Large-Scale Graph Generation
Fuente:
arXiv
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| Autori principali: | , , , , |
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| Natura: | Preprint |
| Pubblicazione: |
2024
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| Soggetti: | |
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| _version_ | 1866910970079608832 |
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| author | Bernecker, Tobias Rehawi, Ghalia Casale, Francesco Paolo Knauer-Arloth, Janine Marsico, Annalisa |
| author_facet | Bernecker, Tobias Rehawi, Ghalia Casale, Francesco Paolo Knauer-Arloth, Janine Marsico, Annalisa |
| contents | Graph generation addresses the problem of generating new graphs that have a data distribution similar to real-world graphs. While previous diffusion-based graph generation methods have shown promising results, they often struggle to scale to large graphs. In this work, we propose ARROW-Diff (AutoRegressive RandOm Walk Diffusion), a novel random walk-based diffusion approach for efficient large-scale graph generation. Our method encompasses two components in an iterative process of random walk sampling and graph pruning. We demonstrate that ARROW-Diff can scale to large graphs efficiently, surpassing other baseline methods in terms of both generation time and multiple graph statistics, reflecting the high quality of the generated graphs. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2408_04461 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Random Walk Diffusion for Efficient Large-Scale Graph Generation Bernecker, Tobias Rehawi, Ghalia Casale, Francesco Paolo Knauer-Arloth, Janine Marsico, Annalisa Machine Learning Social and Information Networks Graph generation addresses the problem of generating new graphs that have a data distribution similar to real-world graphs. While previous diffusion-based graph generation methods have shown promising results, they often struggle to scale to large graphs. In this work, we propose ARROW-Diff (AutoRegressive RandOm Walk Diffusion), a novel random walk-based diffusion approach for efficient large-scale graph generation. Our method encompasses two components in an iterative process of random walk sampling and graph pruning. We demonstrate that ARROW-Diff can scale to large graphs efficiently, surpassing other baseline methods in terms of both generation time and multiple graph statistics, reflecting the high quality of the generated graphs. |
| title | Random Walk Diffusion for Efficient Large-Scale Graph Generation |
| topic | Machine Learning Social and Information Networks |
| url | https://arxiv.org/abs/2408.04461 |