Many important real-world applications-such as social networks or distributed data bases-can be modeled as hypergraphs. In such a model, vertices represent entities-such as users or data records-whereas hyperedges model a group membership of the vertices-such as the authorship in a specific topic or the membership of a data record in a specific replicated shard. To optimize such applications, we need an efficient and effective solution to the NP-hard balanced k-way hypergraph partitioning problem. However, existing hypergraph partitioners that scale to very large graphs do not effectively exploit the hypergraph structure when performing the partitioning decisions. We propose HYPE, a hypergraph partitionier that exploits the neighborhood relations between vertices in the hypergraph using an efficient implementation of neighborhood expansion. HYPE improves partitioning quality by up to 95% and reduces runtime by up to 39% compared to streaming partitioning.
@article{arxiv.1810.11319,
title = {HYPE: Massive Hypergraph Partitioning with Neighborhood Expansion},
author = {Christian Mayer and Ruben Mayer and Sukanya Bhowmik and Lukas Epple and Kurt Rothermel},
journal= {arXiv preprint arXiv:1810.11319},
year = {2018}
}
Comments
To appear in Proceedings of IEEE 2018 International Conference on Big Data (BigData '18), 10 pages