{"url":"/method/bigg","slug":"bigg","name":"BiGG","full_name":"BiGG","full_name_withheld":false,"description_markdown":"**BiGG** is an autoregressive model for generative modeling for sparse graphs. It utilizes sparsity to avoid generating the full adjacency matrix, and 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 $\\Omega(n)$. The approach is based on three key elements: (1) an $O(\\log n)$ process for generating each edge using a binary tree data structure, inspired by R-MAT; (2) a tree-structured autoregressive model for generating the set of edges associated with each node; and (3) an autoregressive model defined over the sequence of nodes.","description_state":"present","introduced_year":null,"introduced_by":{"title":"Scalable Deep Generative Modeling for Sparse Graphs","paper":"/paper/scalable-deep-generative-modeling-for-sparse","first_author":"Hanjun Dai","n_authors":5,"url_abs":null,"archive_paper_url":"https://paperswithcode.com/paper/scalable-deep-generative-modeling-for-sparse"},"source":{"url":"https://arxiv.org/abs/2006.15502v1","title":"Scalable Deep Generative Modeling for Sparse Graphs","url_on_a_paper_host":true},"code_snippet_url":null,"code_snippet_url_on_a_code_host":false,"categories":[{"area":"Graphs","area_id":"graphs","collection":"Graph Models","url":"/methods/category/graph-models","pwc_aliases":[]}],"n_papers_tagged":3,"archive_num_papers":3,"papers_newest_first":[{"paper":null,"title":"A Novel Technique for Query Plan Representation Based on Graph Neural Nets","date":"2024-05-08","arxiv_id":"2405.04814","n_code_links":0,"syntology":null},{"paper":null,"title":"COVRECON: Combining Genome-scale Metabolic Network Reconstruction and Data-driven Inverse Modeling to Reveal Changes in Metabolic Interaction Networks","date":"2023-03-21","arxiv_id":"2303.12526","n_code_links":0,"syntology":null},{"paper":"/paper/scalable-deep-generative-modeling-for-sparse","title":"Scalable Deep Generative Modeling for Sparse Graphs","date":"2020-06-28","arxiv_id":"2006.15502","n_code_links":1,"syntology":null}],"papers_shown":3,"tasks":[{"task":"/task/graph-generation","name":"Graph Generation","papers":1},{"task":"/task/management","name":"Management","papers":1},{"task":"/task/regression-1","name":"regression","papers":1}],"tasks_shown":3,"n_tasks":3,"usage_by_year":[{"year":"2020","papers":1},{"year":"2023","papers":1},{"year":"2024","papers":1}],"row_source":"methods_table","archive":{"source":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28","archive_url":"https://paperswithcode.com/method/bigg"},"syntology_read_at":"2026-09-24T18:15:14+00:00"}