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BiGG

3 papers tagged archive 2025-07-28

Introduced by Hanjun Dai et al. in Scalable Deep Generative Modeling for Sparse Graphs

archive 2025-07-28 Description, source and code snippet are the archive's method entry.

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)logn). Furthermore, during training this autoregressive model can be parallelized with O(logn) synchronization stages, which makes it much more efficient than other autoregressive models that require Ω(n). The approach is based on three key elements: (1) an O(logn) 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.

PaperSource

Papers archive 2025-07-28

3 shown of 3, newest first. Repository counts are the archive's code-links table. A Syntology line states what Syntology ran from that paper's harvested code; it is per sample and not a correctness claim.

Tasks archive 2025-07-28

3 tasks the archive attaches to papers tagged with this method, by distinct papers. A task without a page in the catalog is plain text.

TaskPapers
Graph Generation1
Management1
regression1

Usage over time archive 2025-07-28

Papers per year tagged with BiGG: 2020 to 2024, peak 1 1 0 2020: 1 paper 2020 2021: 0 papers 2021 2022: 0 papers 2022 2023: 1 paper 2023 2024: 1 paper 2024
Papers per year the archive tags with this method, by the paper's archive date (3 dated). Bars are counts, not a trend claim.

Components: the archive holds no method-to-method composition, so PwC's Components table cannot be rebuilt; the Papers list carries no Results column for the same reason (the archive does not join its leaderboard rows to method tags).

Categories archive 2025-07-28

Graph Models

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