Clustering in Hypergraphs to Minimize Average Edge Service Time

Ori Rottenstreich, Haim Kaplan, Avinatan Hassidim

Research output: Contribution to journalArticlepeer-review

Abstract

We study the problem of clustering the vertices of a weighted hypergraph such that on average the vertices of each edge can be covered by a small number of clusters. This problem has many applications, such as for designing medical tests, clustering files on disk servers, and placing network services on servers. The edges of the hypergraph model groups of items that are likely to be needed together, and the optimization criteria that we use can be interpreted as the average delay (or cost) to serve the items of a typical edge. We describe and analyze algorithms for this problem for the case in which the clusters have to be disjoint and for the case where clusters can overlap. The analysis is often subtle and reveals interesting structure and invariants that one can utilize.

Original languageEnglish
Article number40
JournalACM Transactions on Algorithms
Volume16
Issue number3
DOIs
StatePublished - Jun 2020

Bibliographical note

Publisher Copyright:
© 2020 ACM.

Funding

A preliminary version of this article was presented at the European Symposium on Algorithms (ESA), Vienna, Austria, September 2017. The work of O. Rottenstriech was partially supported by the Taub Family Foundation, as well as by the Technion Hiroshi Fujiwara Cyber Security Research Center and the Israel National Cyber Directorate, by the Alon fellowship, by German-Israeli Science Foundation (GIF) Young Scientists Program, and by the Gordon Fund for System Engineering. The work of H. Kaplan was partially supported by Israel Science Foundation (ISF) grant 1595/19 and by grants 1367/2016 from the German-Israeli Science Foundation (GIF). The work of A. Hassidim was partially supported by Israel Science Foundation (ISF) grant 1394/16. Authors’ addresses: O. Rottenstreich, Technion, Technion city, Haifa 3200003, Israel; email: [email protected]; H. Kaplan, Tel Aviv University, Ramat Aviv 6997801, Tel Aviv Israel; email: [email protected]; A. Hassidim, Bar-Ilan University, Ramat Gan, 5290002, Israel, and Google, Tel Aviv, Israel; email: [email protected]. Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]. © 2020 Association for Computing Machinery. 1549-6325/2020/05-ART40 $15.00 https://doi.org/10.1145/3386121

FundersFunder number
German–Israeli Science Foundation
Israel National Cyber Directorate
Taub Family Foundation
Technion Hiroshi Fujiwara Cyber Security Research Center
Israel Science Foundation1367/2016, 1394/16, 1595/19

    Keywords

    • Clustering
    • average cover time
    • hypergraphs
    • set cover

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