TL;DR
This paper presents a highly scalable and parallel method for generating large scale-free graphs based on the Barabasi-Albert model, enabling practical analysis of big data graph problems.
Contribution
It introduces a parallel algorithm that significantly speeds up the generation of large scale-free graphs, making it feasible to produce massive graphs efficiently.
Findings
Generated a Petaedge graph in less than an hour
Demonstrated the method's scalability and efficiency
Enabled large-scale graph analysis for big data applications
Abstract
We explain how massive instances of scale-free graphs following the Barabasi-Albert model can be generated very quickly in an embarrassingly parallel way. This makes this popular model available for studying big data graph problems. As a demonstration, we generated a Petaedge graph in less than an hour.
Peer Reviews
No public reviews on file for this paper yet. If you reviewed it on a platform where reviews are public (OpenReview, ICLR, NeurIPS, ICML), you can paste yours below so the community can read it here.
Code & Models
Videos
No videos yet. Explain this paper in a talk, walkthrough, or lecture? Add one.
