Journal ArticleDOI
A fast parametric maximum flow algorithm and applications
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TLDR
It is shown that the recent maximum flow algorithm of Goldberg and Tarjan can be extended to solve an important class of such parametric maximum flow problems, at the cost of only a constant factor in its worst-case time bound.Abstract:
The classical maximum flow problem sometimes occurs in settings in which the arc capacities are not fixed but are functions of a single parameter, and the goal is to find the value of the parameter such that the corresponding maximum flow or minimum cut satisfies some side condition. Finding the desired parameter value requires solving a sequence of related maximum flow problems. In this paper it is shown that the recent maximum flow algorithm of Goldberg and Tarjan can be extended to solve an important class of such parametric maximum flow problems, at the cost of only a constant factor in its worst-case time bound. Faster algorithms for a variety of combinatorial optimization problems follow from the result.read more
Citations
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Community Structure in Large Networks: Natural Cluster Sizes and the Absence of Large Well-Defined Clusters
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Community Structure in Large Networks: Natural Cluster Sizes and the Absence of Large Well-Defined Clusters
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Statistical properties of community structure in large social and information networks
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Empirical Comparison of Algorithms for Network Community Detection
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References
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A new approach to the maximum-flow problem
TL;DR: An alternative method based on the preflow concept of Karzanov, which runs as fast as any other known method on dense graphs, achieving an O(n) time bound on an n-vertex graph and faster on graphs of moderate density.
Proceedings ArticleDOI
A new approach to the maximum flow problem
TL;DR: By incorporating the dynamic tree data structure of Sleator and Tarjan, a version of the algorithm running in O(nm log(n'/m)) time on an n-vertex, m-edge graph is obtained, as fast as any known method for any graph density and faster on graphs of moderate density.
Journal ArticleDOI
Self-adjusting binary search trees
TL;DR: The splay tree, a self-adjusting form of binary search tree, is developed and analyzed and is found to be as efficient as balanced trees when total running time is the measure of interest.