{"about":{"site":"https://codewithpapers.app","non_affiliation":"Code with Papers and Syntology are not affiliated with, endorsed by, or sponsored by Papers with Code, Meta, or the pwc-archive mirror.","licence":"CC BY-SA 4.0","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","attribution":"https://codewithpapers.app/attribution","modified":"archive material modified by Syntology; see the attribution page"},"url":"/paper/scalable-deep-generative-modeling-for-sparse","title":"Scalable Deep Generative Modeling for Sparse Graphs","arxiv_id":"2006.15502","date":"2020-06-28","proceeding":"ICML 2020 1","authors":["Hanjun Dai","Azade Nazi","Yujia Li","Bo Dai","Dale Schuurmans"],"abstract":"Learning graph generative models is a challenging task for deep learning and has wide applicability to a range of domains like chemistry, biology and social science. However current deep neural methods suffer from limited scalability: for a graph with $n$ nodes and $m$ edges, existing deep neural methods require $\\Omega(n^2)$ complexity by building up the adjacency matrix. On the other hand, many real world graphs are actually sparse in the sense that $m\\ll n^2$. Based on this, we develop a novel autoregressive model, named BiGG, that utilizes this sparsity to avoid generating the full adjacency matrix, and importantly reduces the graph generation time complexity to $O((n + m)\\log n)$. Furthermore, during training this autoregressive model can be parallelized with $O(\\log n)$ synchronization stages, which makes it much more efficient than other autoregressive models that require $\\Omega(n)$. Experiments on several benchmarks show that the proposed approach not only scales to orders of magnitude larger graphs than previously possible with deep autoregressive graph generative models, but also yields better graph generation quality.","url_abs":"https://arxiv.org/abs/2006.15502v1","url_pdf":"https://arxiv.org/pdf/2006.15502v1.pdf","source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","row_kind":"abstracts"},"code_links":[{"paper_slug":"scalable-deep-generative-modeling-for-sparse","repo_url":"https://github.com/google-research/google-research","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":0,"framework":"tf","reach":null}],"tasks":[{"task_slug":"graph-generation","task_name":"Graph Generation"}],"methods":[{"method_slug":"bigg","method_name":"BiGG"}],"datasets_introduced":[],"methods_introduced":[{"slug":"bigg","name":"BiGG","full_name":"BiGG"}],"results":[],"syntology":{"atlas_url":"https://app.syntology.ai/?focus=2006.15502","mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}