Abstract
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.
Original language | English |
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Pages (from-to) | 145-161 |
Number of pages | 17 |
Journal | Annals of Operations Research |
Volume | 216 |
Issue number | 1 |
DOIs | |
State | Published - 1 Jan 2014 |
Externally published | Yes |
Keywords
- Clique
- Cluster detection
- Graph-based data mining
- Quasi-clique