A critical point for random graphs with a given degree sequence
Random Graphs 93 Proceedings of the sixth international seminar on Random graphs and probabilistic methods in combinatorics and computer science
On maximum clique problems in very large graphs
External memory algorithms
Computers and Intractability: A Guide to the Theory of NP-Completeness
Computers and Intractability: A Guide to the Theory of NP-Completeness
Handbook of massive data sets
A better list heuristic for vertex cover
Information Processing Letters
Algorithms and Data Structures for External Memory
Algorithms and Data Structures for External Memory
Journal of Discrete Algorithms
Experimental evaluation of a tree decomposition-based algorithm for vertex cover on planar graphs
Discrete Applied Mathematics - Structural decompositions, width parameters, and graph labelings (DAS 5)
Vertex cover approximations on random graphs
WEA'07 Proceedings of the 6th international conference on Experimental algorithms
Analytical and experimental comparison of six algorithms for the vertex cover problem
Journal of Experimental Algorithmics (JEA)
Minimum vertex cover in rectangle graphs
ESA'10 Proceedings of the 18th annual European conference on Algorithms: Part I
Kernelization as heuristic structure for the vertex cover problem
ANTS'06 Proceedings of the 5th international conference on Ant Colony Optimization and Swarm Intelligence
Efficient and simple generation of random simple connected graphs with prescribed degree sequence
COCOON'05 Proceedings of the 11th annual international conference on Computing and Combinatorics
Note: A new lower bound on the independence number of graphs
Discrete Applied Mathematics
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We present in this paper an experimental study of six heuristics for a well-studied NP-complete graph problem: the vertex cover. These algorithms are adapted to process huge graphs. Indeed, executed on a current laptop computer, they offer reasonable CPU running times (between twenty seconds and eight hours) on graphs for which sizes are between 200 ·106 and 100 ·109 vertices and edges. We have run algorithms on specific graph families (we propose generators) and also on random power law graphs. Some of these heuristics can produce good solutions. We give here a comparison and an analysis of results obtained on several instances, in terms of quality of solutions and complexity, including running times.