HYPE: Massive Hypergraph Partitioning with Neighborhood Expansion

TL;DR

HYPE is a scalable hypergraph partitioning algorithm that leverages neighborhood expansion to significantly improve partition quality and efficiency for large hypergraphs, addressing the NP-hard problem effectively.

Contribution

HYPE introduces a novel neighborhood expansion technique that enhances hypergraph partitioning quality and speed, outperforming existing streaming methods.

Findings

Improves partitioning quality by up to 95%.

Reduces runtime by up to 39%.

Effectively exploits hypergraph structure for large graphs.

Abstract

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.