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Selected Works of Kai Wang
Contribution to Book
Toward an Efficient, Highly Scalable Maximum Clique Solver for Massive Graphs
Proceedings of the IEEE International Conference on Big Data (2014)
  • Ronald D. Hagan, University of Tennessee, Knoxville
  • Charles A. Philips, University of Tennessee, Knoxville
  • Kai Wang, Georgia Southern University
  • Gary L. Rogers, University of Tennessee, Knoxville
  • Michael A. Langston, University of Tennessee, Knoxville
Link
Abstract
As the size of available data sets grows, so too does the demand for efficient parallel algorithms that will yield the solution to complex combinatorial problems on graphs that may be too large to fit entirely in memory. In previous work, we have provided a set of out-of-core algorithms to solve one of the central examples of such a problem, maximum clique. In this paper, we review the algorithms and report on our ongoing work to use them as a starting point for an optimized, highly scalable implementation of a maximum clique solver.
Keywords
  • Algorithm design and analysis,
  • Parallel algorithms,
  • Roads,
  • Conferences,
  • Memory management,
  • Big data,
  • Optimization,
  • maximum clique,
  • big data,
  • parallel graph algorithms,
  • out-of-core
Disciplines
Publication Date
October 27, 2014
Publisher
IEEE
ISBN
978-1-4799-5666-1
DOI
10.1109/BigData.2014.7004370
Citation Information
Ronald D. Hagan, Charles A. Philips, Kai Wang, Gary L. Rogers, et al.. "Toward an Efficient, Highly Scalable Maximum Clique Solver for Massive Graphs" Washington, D.C.Proceedings of the IEEE International Conference on Big Data (2014) p. 41 - 45
Available at: http://works.bepress.com/kai-wang/11/