We study the parameterized complexity of a broad class of problems called "local graph partitioningproblems" that includes the classical fixed cardinality problems as max -vertex cover, -densest subgraph, e...
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We study the parameterized complexity of a broad class of problems called "local graph partitioningproblems" that includes the classical fixed cardinality problems as max -vertex cover, -densest subgraph, etc. By developing a technique that we call "greediness-for-parameterization", we obtain fixed parameter algorithms with respect to a pair of parameters , the size of the solution (but not its value) and , the maximum degree of the input graph. In particular, greediness-for-parameterization improves asymptotic running times for these problems upon random separation (that is a special case of color coding) and is more intuitive and simple. Then, we show how these results can be easily extended for getting standard-parameterization results (i.e., with parameter the value of the optimal solution) for a well known local graph partitioning problem.
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