On computing all suboptimal alignments
Information Sciences: an International Journal
An efficient algorithm for the length-constrained heaviest path problem on a tree
Information Processing Letters
Introduction to the Theory of Computation
Introduction to the Theory of Computation
Journal of Computer and System Sciences - Computational biology 2002
WABI '02 Proceedings of the Second International Workshop on Algorithms in Bioinformatics
Linear-time algorithm for finding a maximum-density segment of a sequence
Information Processing Letters
An Optimal Algorithm for the Maximum-Density Segment Problem
SIAM Journal on Computing
Location Awareness in Unstructured Peer-to-Peer Systems
IEEE Transactions on Parallel and Distributed Systems
Improving Unstructured Peer-to-Peer Systems by Adaptive Connection Establishment
IEEE Transactions on Computers
Fast and low-cost search schemes by exploiting localities in P2P networks
Journal of Parallel and Distributed Computing
Improving Query Response Delivery Quality in Peer-to-Peer Systems
IEEE Transactions on Parallel and Distributed Systems
An Effective P2P Search Scheme to Exploit File Sharing Heterogeneity
IEEE Transactions on Parallel and Distributed Systems
LightFlood: Minimizing Redundant Messages and Maximizing Scope of Peer-to-Peer Search
IEEE Transactions on Parallel and Distributed Systems
An improved algorithm for finding a length-constrained maximum-density subtree in a tree
Information Processing Letters
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Given a tree T=(V,E) of n nodes such that each node v is associated with a value-weight pair (val v ,w v ), where value val v is a real number and weight w v is a non-negative integer, the density of T is defined as $\frac{\sum_{v\in V}{\mathit{val}}_{v}}{\sum_{v\in V}w_{v}}$ . A subtree of T is a connected subgraph (V驴,E驴) of T, where V驴驴V and E驴驴E. Given two integers w min驴 and w max驴, the weight-constrained maximum-density subtree problem on T is to find a maximum-density subtree T驴=(V驴,E驴) satisfying w min驴驴驴 v驴V驴 w v 驴w max驴. In this paper, we first present an O(w max驴 n)-time algorithm to find a weight-constrained maximum-density path in a tree T, and then present an O(w max驴 2 n)-time algorithm to find a weight-constrained maximum-density subtree in T. Finally, given a node subset S驴V, we also present an O(w max驴 2 n)-time algorithm to find a weight-constrained maximum-density subtree in T which covers all the nodes in S.