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Computing Critical Nodes in Directed Graphs

Published: 18 July 2018 Publication History

Abstract

We consider the critical node detection problem (CNDP) in directed graphs, which can be defined as follows. Given a directed graph G and a parameter k, we wish to remove a subset S of at most k vertices of G such that the residual graph GS has minimum pairwise strong connectivity. This problem is NP-hard, and thus we are interested in practical heuristics. In this article, we apply the framework of Georgiadis et al. (SODA 2017) and provide a sophisticated linear-time algorithm for the k=1 case. Based on this algorithm, we provide an efficient heuristic for the general case. Then, we conduct a thorough experimental evaluation of various heuristics for CNDP. Our experimental results suggest that our heuristic performs very well in practice, both in terms of running time and of solution quality.

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Published In

cover image ACM Journal of Experimental Algorithmics
ACM Journal of Experimental Algorithmics  Volume 23, Issue
Special Issue ALENEX 2017
2018
368 pages
ISSN:1084-6654
EISSN:1084-6654
DOI:10.1145/3178547
Issue’s Table of Contents
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Association for Computing Machinery

New York, NY, United States

Publication History

Published: 18 July 2018
Accepted: 01 March 2018
Revised: 01 December 2017
Received: 01 May 2017
Published in JEA Volume 23

Author Tags

  1. Combinatorial optimization
  2. NP-hard
  3. critical nodes
  4. directed graphs
  5. strong articulation points
  6. strong connectivity

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  • Italian Ministry of Education, University and Research
  • AMANDA (Algorithmics for MAssive and Networked DAta)

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