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Theoretical Computer Science
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Theoretical Computer Science
Article . 2007
License: Elsevier Non-Commercial
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Theoretical Computer Science
Article . 2007 . Peer-reviewed
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Hardness and approximation results for Black Hole Search in arbitrary networks

Authors: Klasing, Ralf; Markou, Euripides; Radzik, Tomasz; Sarracco, Fabiano;

Hardness and approximation results for Black Hole Search in arbitrary networks

Abstract

AbstractA black hole is a highly harmful stationary process residing in a node of a network and destroying all mobile agents visiting the node without leaving any trace. The Black Hole Search is the task of locating all black holes in a network by exploring it with mobile agents. We consider the problem of designing the fastest Black Hole Search, given the map of the network and the starting node. We study the version of this problem that assumes that there is at most one black hole in the network and there are two agents, which move in synchronized steps. We prove that this problem is NP-hard in arbitrary graphs (even in planar graphs), solving an open problem stated in [J. Czyzowicz, D. Kowalski, E. Markou, A. Pelc, Searching for a black hole in tree networks, in: Proc. 8th Int. Conf. on Principles of Distributed Systems, OPODIS 2004, 2004, pp. 34–35. Also: Springer LNCS, vol. 3544, pp. 67–80]. We also give a 338-approximation algorithm, showing the first non-trivial approximation ratio upper bound for this problem. Our algorithm follows a natural approach of exploring networks via spanning trees. We prove that this approach cannot lead to an approximation ratio bound better than 3/2.

Keywords

Mobile agent, Graph exploration, Black Hole Search, NP-hardness, Approximation algorithm, 004, Theoretical Computer Science, Computer Science(all)

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citations
This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Citations provided by BIP!
popularity
This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.
BIP!Popularity provided by BIP!
influence
This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Influence provided by BIP!
impulse
This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.
BIP!Impulse provided by BIP!
45
Average
Top 10%
Top 10%
hybrid