An Incremental Reseeding Strategy for Clustering

  • Xavier BressonEmail author
  • Huiyi Hu
  • Thomas Laurent
  • Arthur Szlam
  • James von Brecht
Conference paper
Part of the Mathematics and Visualization book series (MATHVISUAL)


We propose an easy-to-implement and highly parallelizable algorithm for multiway graph partitioning. The algorithm proceeds by alternating three simple routines in an iterative fashion: diffusion , thresholding, and random sampling. We demonstrate experimentally that the proper combination of these ingredients leads to an algorithm that achieves state-of-the-art performance in terms of cluster purity on standard benchmark data sets. We also describe a coarsen, cluster and refine approach similar to Dhillon et al. (IEEE Trans Pattern Anal Mach Intell 29(11):1944–1957, 2007) and Karypis and Kumar (SIAM J Sci Comput 20(1):359–392, 1998) that removes an order of magnitude from the runtime of our algorithm while still maintaining competitive accuracy.



XB is supported by NRF Fellowship NRFF2017-10.


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Copyright information

© Springer International Publishing AG, part of Springer Nature 2018

Authors and Affiliations

  • Xavier Bresson
    • 1
    Email author
  • Huiyi Hu
    • 2
  • Thomas Laurent
    • 3
  • Arthur Szlam
    • 4
  • James von Brecht
    • 5
  1. 1.School of Computer Science and EngineeringNanyang Technological UniversitySingaporeSingapore
  2. 2.Google IncMountain ViewUSA
  3. 3.Department of MathematicsLoyola Marymount UniversityLos AngelesUSA
  4. 4.Facebook AI ResearchNew YorkUSA
  5. 5.Department of MathematicsCalifornia State UniversityLong BeachUSA

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