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ROLL: Fast In-Memory Generation of Gigantic Scale-free Networks

Summary: ROLL-tree uses an in-memory roulette-wheel data structure to enable exact Barabási–Albert preferential attachment for scale-free graph generation. ≈1000× faster than state-of-the-art on a single PC; generates 1.1B nodes and 6.6B edges in 62 minutes, and generalizes to other rich-get-richer growth models. (summarized by gpt-5-nano on Feb 09 2026)

Paper ID
5155
Venue
SIGMOD
Year
2016
Pagerank
4.8780906e-05
Overall Rank
6,974 | 51.49%
DOI
10.1145/2882903.2882964

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