A probabilistic analysis of the maximal covering location problem
Discrete Applied Mathematics - Special issue: local optimization
The hardness of approximation: gap location
Computational Complexity
Maintaining a topological order under edge insertions
Information Processing Letters
Approximation algorithms for NP-hard problems
Towards adaptive Web sites: conceptual framework and case study
WWW '99 Proceedings of the eighth international conference on World Wide Web
Incremental evaluation of computational circuits
SODA '90 Proceedings of the first annual ACM-SIAM symposium on Discrete algorithms
The budgeted maximum coverage problem
Information Processing Letters
Introduction to algorithms
Approximation algorithms for maximization problems arising in graph partitioning
Journal of Algorithms
Strategies for Hotlink Assignments
ISAAC '00 Proceedings of the 11th International Conference on Algorithms and Computation
WG '01 Proceedings of the 27th International Workshop on Graph-Theoretic Concepts in Computer Science
On the Optimal Placement of Web Proxies in the Internet: The Linear Topology
HPN '98 Proceedings of the IFIP TC-6 Eigth International Conference on High Performance Networking
Enhancing hyperlink structure for improving web performance
Enhancing hyperlink structure for improving web performance
Efficient algorithms for the hotlink assignment problem: the worst case search
ISAAC'04 Proceedings of the 15th international conference on Algorithms and Computation
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Consider a rooted directed acyclic graph G = (V,E) with root r, representing a collection V of web pages connected via a set E of hyperlinks. Each node v is associated with the probability that a user wants to access the node v. The access cost is defined as the expected number of steps required to reach a node from the root r. A bookmark is an additional shortcut from r to a node of G, which may reduce the access cost. The bookmark assignment problem is to find a set of bookmarks that achieves the greatest improvement in the access cost. For the problem, the paper presents a polynomial time approximation algorithm with factor (1-1/e), and shows that there exists no polynomial time approximation algorithm with a better constant factor than (1-1/e) unless NP ⊆ DT IME(NO(log logN)), where N is the size of the inputs.