A branch-and-bound approach for maximum quasi-cliques

作者：Pajouh Foad Mahdavi; Miao Zhuqi; Balasundaram Balabhaskar^*

来源：Annals of Operations Research, 2014, 216(1): 145-161.

DOI：10.1007/s10479-012-1242-y

• Clique
• Quasi-clique
• Cluster detection
• Graph-based data mining

Detecting quasi-cliques in graphs is a useful tool for detecting dense clusters in graph-based data mining. Particularly in large-scale data sets that are error-prone, cliques are overly restrictive and impractical. Quasi-clique detection has been accomplished using heuristic approaches in various applications of graph-based data mining in protein interaction networks, gene co-expression networks, and telecommunication networks. Quasi-cliques are not hereditary, in the sense that every subset of a quasi-clique need not be a quasi-clique. This lack of heredity introduces interesting challenges in the development of exact algorithms to detect maximum cardinality quasi-cliques. The only exact approaches for this problem are limited to two mixed integer programming formulations that were recently proposed in the literature. The main contribution of this article is a new combinatorial branch-and-bound algorithm for the maximum quasi-clique problem.

• 出版日期2014-5