Identifying risk-averse low-diameter clusters in graphs with stochastic vertex weights-Reference-Cited by-同舟云学术

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Identifying risk-averse low-diameter clusters in graphs with stochastic vertex weights

Published:2016-05-04 Issue:1 Volume:262 Page:89-108
ISSN:0254-5330
Container-title:Annals of Operations Research
language:en
Short-container-title:Ann Oper Res

Author:

Rysz Maciej,Pajouh Foad Mahdavi,Krokhmal Pavlo,Pasiliao Eduardo L.

Funder

Air Force Office of Scientific Research

Defense Threat Reduction Agency

U.S. Air Force

Publisher

Springer Science and Business Media LLC

Subject

Management Science and Operations Research,General Decision Sciences

Link

http://link.springer.com/article/10.1007/s10479-016-2212-6/fulltext.html

Reference47 articles.

1. Abello, J., Pardalos, P., & Resende, M. (1999). On maximum clique problems in very large graphs. In Abello, J. and Vitter, J. (Eds.) External memory algorithms and visualization, volume 50 of DIMACS Series on Discrete Mathematics and Theoretical Computer Science, pp. 119–130. Providence, RI: American Mathematical Society.

2. Abello, J., Resende, M., & Sudarsky, S. (2002). Massive quasi-clique detection. In S. Rajsbaum (Ed.), LATIN 2002: Theoretical informatics (pp. 598–612). London: Springer.

3. Alba, R. D. (1973). A graph-theoretic definition of a sociometric clique. Journal of Mathematical Sociology, 3, 3–113.

4. Aneja, Y. P., Chandrasekaran, R., & Nair, K. P. K. (2001). Maximizing residual flow under an arc destruction. Networks, 38(4), 194–198.

5. Artzner, P., Delbaen, F., Eber, J.-M., & Heath, D. (1999). Coherent measures of risk. Mathematical Finance, 9(3), 203–228.

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