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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
. - S. 458-467
DOI: https://doi.org/10.1109/BigData.2018.8621968
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 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.
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| Publikationsform: | Aufsatz in einem Buch |
|---|---|
| Beteiligte Institutionen (z.B. Veranstalter, Sponsor): | IEEE |
| Begutachteter Beitrag: | Ja |
| Institutionen der Universität: | Fakultäten Fakultäten > Fakultät für Mathematik, Physik und Informatik Fakultäten > Fakultät für Mathematik, Physik und Informatik > Institut für Informatik > Lehrstuhl Data Systems Fakultäten > Fakultät für Mathematik, Physik und Informatik > Institut für Informatik > Lehrstuhl Data Systems > Lehrstuhl Data Systems - Univ.-Prof. Dr. Ruben Mayer Fakultäten > Fakultät für Mathematik, Physik und Informatik > Institut für Informatik |
| Titel an der UBT entstanden: | Nein |
| Themengebiete aus DDC: | 000 Informatik,Informationswissenschaft, allgemeine Werke > 004 Informatik |
| Eingestellt am: | 27 Apr 2023 07:47 |
| Letzte Änderung: | 05 Feb 2024 07:37 |
| URI: | https://eref.uni-bayreuth.de/id/eprint/76033 |