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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
5452
Venue
SIGMOD
Year
2017
Pagerank
5.8741671e-05
Overall Rank
6,457 | 55.70%
DOI
10.1145/3035918.3064014

Incoming Non-self Citations Over Time

Authors

BibTeX Citation

@inproceedings{park_sigmod17,
        title = {{TrillionG: A Trillion-scale Synthetic Graph Generator using a Recursive Vector Model}},
        author = {Park, Himchan and Kim, Min-Soo},
        series = {{SIGMOD} '17},
        booktitle = {Proceedings of the {ACM} {SIGMOD} International Conference on Management of Data},
        publisher = {Association for Computing Machinery},
        doi = {10.1145/3035918.3064014},
        url = {https://dl.acm.org/doi/10.1145/3035918.3064014},
        year = {2017}
}

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