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HYPE : Massive hypergraph partitioning with neighborhood expansion

Title data

Mayer, Christian ; Mayer, Ruben ; Bhowmik, Sukanya ; Epple, Lukas ; Rothermel, Kurt:
HYPE : Massive hypergraph partitioning with neighborhood expansion.
In: 2018 IEEE International Conference on Big Data. - Piscataway, NJ : IEEE , 2018 . - pp. 458-467
DOI: https://doi.org/10.1109/BigData.2018.8621968

Abstract in another language

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 hy-pergraph 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.

Further data

Item Type: Article in a book
Related institutions (e.g. sponsor, organisator): IEEE
Refereed: Yes
Institutions of the University: Faculties
Faculties > Faculty of Mathematics, Physics und Computer Science
Faculties > Faculty of Mathematics, Physics und Computer Science > Department of Computer Science > Chair Data Systems
Faculties > Faculty of Mathematics, Physics und Computer Science > Department of Computer Science > Chair Data Systems > Chair Data Systems - Univ.-Prof. Dr. Ruben Mayer
Faculties > Faculty of Mathematics, Physics und Computer Science > Department of Computer Science
Result of work at the UBT: No
DDC Subjects: 000 Computer Science, information, general works > 004 Computer science
Date Deposited: 27 Apr 2023 07:47
Last Modified: 05 Feb 2024 07:37
URI: https://eref.uni-bayreuth.de/id/eprint/76033