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TrillionG: A Trillion-scale Synthetic Graph Generator using a Recursive Vector Model

Summary: Disk-based, memory-light generator capable of trillion-edge graphs on commodity clusters (e.g., 10 PCs). Proposes a recursive vector model that generalizes RMAT/Kronecker to a scope-based framework, enabling fast, scalable generation and richer graph semantics with orders-of-magnitude gains. (summarized by gpt-5-nano on Feb 09 2026)

Paper ID
5390
Venue
SIGMOD
Year
2017
Pagerank
4.968252e-05
Overall Rank
6,668 | 53.62%
DOI
10.1145/3035918.3064014

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