@inproceedings{5d675a0b58cb4e9e87ea24d810faa52a,
title = "Toward an efficient, highly scalable maximum clique solver for massive graphs",
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, maximum clique, out-of-core, parallel graph algorithms",
author = "Hagan, \{Ronald D.\} and Philips, \{Charles A.\} and Kai Wang and Rogers, \{Gary L.\} and Langston, \{Michael A.\}",
note = "Publisher Copyright: {\textcopyright} 2014 IEEE.; 2nd IEEE International Conference on Big Data, IEEE Big Data 2014 ; Conference date: 27-10-2014 Through 30-10-2014",
year = "2014",
doi = "10.1109/BigData.2014.7004370",
language = "English",
series = "Proceedings - 2014 IEEE International Conference on Big Data, IEEE Big Data 2014",
publisher = "Institute of Electrical and Electronics Engineers Inc.",
pages = "41--45",
editor = "Wo Chang and Jun Huan and Nick Cercone and Saumyadipta Pyne and Vasant Honavar and Jimmy Lin and Hu, \{Xiaohua Tony\} and Charu Aggarwal and Bamshad Mobasher and Jian Pei and Raghunath Nambiar",
booktitle = "Proceedings - 2014 IEEE International Conference on Big Data, IEEE Big Data 2014",
address = "United States",
}