Comparing the Sensitivity of Social Networks, Web Graphs, and Random Graphs with Respect to Vertex Removal

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The sensitivity of networks to the removal of vertices has been studied extensively over the last 15 years. A common approach to measuring this sensitivity is (i) successively removing vertices following a specific removal strategy and (ii) comparing the original and the modified network using a specific comparison method. In this paper we apply a wide range of removal strategies and comparison methods in order to study the sensitivity of medium-sized networks from the real world and randomly generated networks. In the first part of our study we observe that social networks and web graphs differ in sensitivity. When removing vertices, social networks are robust, web graphs are not. This effect is consistent with the work of Boldi et al. who analyzed very large social networks and web graphs. For randomly generated networks we find that their sensitivity depends significantly on the comparison method. The choice of removal strategy has surprisingly marginal impact on the sensitivity for removal strategies derived from common centrality measures. However, the removal strategy has a strong impact when removing the vertices in random order.
Original languageEnglish
Title of host publication2015 11th International Conference on Signal-Image Technology & Internet-Based Systems (SITIS)
EditorsKokou Yetongnon, Albert Dipanda, Richard Chbeir
Number of pages8
PublisherIEEE - Institute of Electrical and Electronics Engineers Inc.
Publication date05.02.2016
Article number7400603
ISBN (Electronic)978-1-4673-9721-6/
Publication statusPublished - 05.02.2016
Event International Conference on Signal-Image Technology & Internet-Based Systems 2015 - Bangkok, Thailand
Duration: 23.11.201527.11.2015
Conference number: 11

    Research areas

  • Business informatics - centrality measure, complex networks, random graphs, robustness analysis