Nothing Special   »   [go: up one dir, main page]

skip to main content
10.1145/3670684.3673421acmconferencesArticle/Chapter ViewAbstractPublication PagesspaaConference Proceedingsconference-collections
poster

TeraHAC: Hierarchical Agglomerative Clustering of Trillion-Edge Graphs (Abstract)

Published: 26 July 2024 Publication History

Abstract

This paper presents a new distributed algorithm for hierarchical agglomerative clustering (HAC), a widely-used clustering algorithm known for its high quality in a variety of applications.

References

[1]
Mohammadhossein Bateni, Soheil Behnezhad, Mahsa Derakhshan, Mohammad- Taghi Hajiaghayi, Raimondas Kiveris, Silvio Lattanzi, and Vahab Mirrokni. Affinity clustering: Hierarchical clustering at scale. In NeurIPS, 2017.
[2]
MohammadHossein Bateni, Laxman Dhulipala, Willem Fletcher, Kishen N Gowda, D Ellis Hershkowitz, Rajesh Jayaram, and Jakub Lacki. Efficient centroid-linkage clustering. arXiv preprint arXiv:2406.05066, 2024.
[3]
MohammadHossein Bateni, Laxman Dhulipala, Kishen N Gowda, D Ellis Hershkowitz, Rajesh Jayaram, and Jakub Lacki. It's hard to HAC with average linkage! arXiv preprint arXiv:2404.14730, 2024.
[4]
J-P Benzécri. Construction d'une classification ascendante hiérarchique par la recherche en chaîne des voisins réciproques. L'analyse des données, 1982.
[5]
Laxman Dhulipala, David Eisenstat, Jakub Lacki, Vahab Mirrokni, and Jessica Shi. Hierarchical agglomerative graph clustering in nearly-linear time. In ICML, 2021.
[6]
Laxman Dhulipala, David Eisenstat, Jakub Lacki, Vahab Mirrokni, and Jessica Shi. Hierarchical agglomerative graph clustering in poly-logarithmic depth. In NeurIPS, 2022.
[7]
Laxman Dhulipala, Jakub Lacki, Jason Lee, and Vahab Mirrokni. TeraHAC: Hierarchical agglomerative clustering of trillion-edge graphs. SIGMOD, 2023.
[8]
Nicholas Monath, Kumar Avinava Dubey, Guru Guruganesh, Manzil Zaheer, et al. Scalable hierarchical agglomerative clustering. In KDD, 2021.
[9]
Baris Sumengen, Anand Rajagopalan, Gui Citovsky, David Simcha, et al. Scaling hierarchical agglomerative clustering to billion-sized datasets. arXiv preprint arXiv:2105.11653, 2021.

Index Terms

  1. TeraHAC: Hierarchical Agglomerative Clustering of Trillion-Edge Graphs (Abstract)

      Recommendations

      Comments

      Please enable JavaScript to view thecomments powered by Disqus.

      Information & Contributors

      Information

      Published In

      cover image ACM Conferences
      HOPC'24: Proceedings of the 2024 ACM Workshop on Highlights of Parallel Computing
      June 2024
      47 pages
      ISBN:9798400707001
      DOI:10.1145/3670684
      Permission to make digital or hard copies of part or all 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 third-party components of this work must be honored. For all other uses, contact the Owner/Author.

      Sponsors

      Publisher

      Association for Computing Machinery

      New York, NY, United States

      Publication History

      Published: 26 July 2024

      Check for updates

      Author Tags

      1. distributed graph algorithms
      2. graph clustering
      3. parallel graph algorithms

      Qualifiers

      • Poster

      Conference

      SPAA '24
      Sponsor:

      Contributors

      Other Metrics

      Bibliometrics & Citations

      Bibliometrics

      Article Metrics

      • 0
        Total Citations
      • 15
        Total Downloads
      • Downloads (Last 12 months)15
      • Downloads (Last 6 weeks)4
      Reflects downloads up to 24 Sep 2024

      Other Metrics

      Citations

      View Options

      Get Access

      Login options

      View options

      PDF

      View or Download as a PDF file.

      PDF

      eReader

      View online with eReader.

      eReader

      Media

      Figures

      Other

      Tables

      Share

      Share

      Share this Publication link

      Share on social media