Title :
Toward an efficient, highly scalable maximum clique solver for massive graphs
Author :
Hagan, Ronald D. ; Phillips, Charles A. ; Wang, Kai ; Rogers, Gary L. ; Langston, Michael A.
Author_Institution :
Dept. of Electr. Eng. & Comput. Sci., Univ. of Tennessee, Knoxville, TN, USA
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 :
Big Data; graph theory; parallel algorithms; complex combinatorial problem; data set; massive graph; maximum clique solver; out-of-core algorithm; parallel algorithm; Algorithm design and analysis; Big data; Conferences; Memory management; Optimization; Parallel algorithms; Roads; big data; maximum clique; out-of-core; parallel graph algorithms;
Conference_Titel :
Big Data (Big Data), 2014 IEEE International Conference on
Conference_Location :
Washington, DC
DOI :
10.1109/BigData.2014.7004370
