1. //
  2. Start
  3. //
  4. Publications
  5. Comparing the Sensitivity of Social Netw...
  6. Output formats
  7. //

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

Research output: Contributions to collected editions/works › Article in conference proceedings › Research › peer-review

Standard

Comparing the Sensitivity of Social Networks, Web Graphs, and Random Graphs with Respect to Vertex Removal. / Martin, Christoph; Niemeyer, Peter.
2015 11th International Conference on Signal-Image Technology & Internet-Based Systems (SITIS). ed. / Kokou Yetongnon; Albert Dipanda; Richard Chbeir. IEEE - Institute of Electrical and Electronics Engineers Inc., 2016. p. 460-467 7400603 (Proceedings - 11th International Conference on Signal-Image Technology and Internet-Based Systems, SITIS 2015).

Research output: Contributions to collected editions/works › Article in conference proceedings › Research › peer-review

Harvard

Martin, C & Niemeyer, P 2016, Comparing the Sensitivity of Social Networks, Web Graphs, and Random Graphs with Respect to Vertex Removal. in K Yetongnon, A Dipanda & R Chbeir (eds), 2015 11th International Conference on Signal-Image Technology & Internet-Based Systems (SITIS)., 7400603, Proceedings - 11th International Conference on Signal-Image Technology and Internet-Based Systems, SITIS 2015, IEEE - Institute of Electrical and Electronics Engineers Inc., pp. 460-467, International Conference on Signal-Image Technology & Internet-Based Systems 2015, Bangkok, Thailand, 23.11.15. https://doi.org/10.1109/SITIS.2015.22

APA

Martin, C., & Niemeyer, P. (2016). Comparing the Sensitivity of Social Networks, Web Graphs, and Random Graphs with Respect to Vertex Removal. In K. Yetongnon, A. Dipanda, & R. Chbeir (Eds.), 2015 11th International Conference on Signal-Image Technology & Internet-Based Systems (SITIS) (pp. 460-467). Article 7400603 (Proceedings - 11th International Conference on Signal-Image Technology and Internet-Based Systems, SITIS 2015). IEEE - Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/SITIS.2015.22

Vancouver

Martin C, Niemeyer P. Comparing the Sensitivity of Social Networks, Web Graphs, and Random Graphs with Respect to Vertex Removal. In Yetongnon K, Dipanda A, Chbeir R, editors, 2015 11th International Conference on Signal-Image Technology & Internet-Based Systems (SITIS). IEEE - Institute of Electrical and Electronics Engineers Inc. 2016. p. 460-467. 7400603. (Proceedings - 11th International Conference on Signal-Image Technology and Internet-Based Systems, SITIS 2015). doi: 10.1109/SITIS.2015.22

Bibtex

@inbook{be01e75fb3ec434f8df293a5a0362136,
title = "Comparing the Sensitivity of Social Networks, Web Graphs, and Random Graphs with Respect to Vertex Removal",
abstract = "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.",
keywords = "Business informatics, centrality measure, complex networks, random graphs, robustness analysis",
author = "Christoph Martin and Peter Niemeyer",
year = "2016",
month = feb,
day = "5",
doi = "10.1109/SITIS.2015.22",
language = "English",
series = "Proceedings - 11th International Conference on Signal-Image Technology and Internet-Based Systems, SITIS 2015",
publisher = "IEEE - Institute of Electrical and Electronics Engineers Inc.",
pages = "460--467",
editor = "Kokou Yetongnon and Albert Dipanda and Richard Chbeir",
booktitle = "2015 11th International Conference on Signal-Image Technology & Internet-Based Systems (SITIS)",
address = "United States",
note = " International Conference on Signal-Image Technology & Internet-Based Systems 2015, SITIS 2015 ; Conference date: 23-11-2015 Through 27-11-2015",
url = "http://www.sitis-conf.org/past-conferences/www.sitis-conf.org-2015/index.php.html",

}

RIS

TY - CHAP

T1 - Comparing the Sensitivity of Social Networks, Web Graphs, and Random Graphs with Respect to Vertex Removal

AU - Martin, Christoph

AU - Niemeyer, Peter

N1 - Conference code: 11

PY - 2016/2/5

Y1 - 2016/2/5

N2 - 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.

AB - 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.

KW - Business informatics

KW - centrality measure

KW - complex networks

KW - random graphs

KW - robustness analysis

UR - http://www.scopus.com/inward/record.url?scp=84966341118&partnerID=8YFLogxK

U2 - 10.1109/SITIS.2015.22

DO - 10.1109/SITIS.2015.22

M3 - Article in conference proceedings

T3 - Proceedings - 11th International Conference on Signal-Image Technology and Internet-Based Systems, SITIS 2015

SP - 460

EP - 467

BT - 2015 11th International Conference on Signal-Image Technology & Internet-Based Systems (SITIS)

A2 - Yetongnon, Kokou

A2 - Dipanda, Albert

A2 - Chbeir, Richard

PB - IEEE - Institute of Electrical and Electronics Engineers Inc.

T2 - International Conference on Signal-Image Technology & Internet-Based Systems 2015

Y2 - 23 November 2015 through 27 November 2015

ER -

Other publications by the same author(s)

On the impact of network size and average degree on the robustness of centrality measures

Martin, C. & Niemeyer, P., 20.10.2021, In: Network Science. 9, S1, p. S61-S82 22 p.

Research output: Journal contributions › Journal articles › Research › peer-review

The Role of Network Size for the Robustness of Centrality Measures

Martin, C. & Niemeyer, P., 01.01.2020, Complex Networks and Their Applications VIII: Volume 1 Proceedings of the Eighth International Conference on Complex Networks and Their Applications COMPLEX NETWORKS 2019. Cherifi, H., Gaito, S., Mendes, J. F., Moro, E. & Rocha, L. M. (eds.). Cham: Springer Schweiz, Vol. 1. p. 40-51 12 p. (Studies in Computational Intelligence; vol. 881).

Research output: Contributions to collected editions/works › Article in conference proceedings › Research › peer-review

Influence of measurement errors on networks: Estimating the robustness of centrality measures

Martin, C. & Niemeyer, P., 01.06.2019, In: Network Science. 7, 2, p. 180-195 16 p.

Research output: Journal contributions › Journal articles › Research › peer-review

Multikonferenz Wirtschaftsinformatik 2018: Data driven X — Turning Data into Value

Drews, P. (Editor), Funk, B. (Editor), Niemeyer, P. (Editor) & Xie, L. (Editor), 14.03.2018, Lüneburg: Institut für Wirtschaftsinformatik. 2094 p.

Research output: Books and anthologies › Conference proceedings › Research

The impact of partially missing communities on the reliability of centrality measures

Martin, C., 2018, Complex Networks & Their Applications VI: Proceedings of Complex Networks 2017 (The 6th International Conference on Complex Networks and Their Applications). Springer Verlag, Vol. 1. p. 41-52 12 p. (Studies in Computational Intelligence; vol. 689).

Research output: Contributions to collected editions/works › Article in conference proceedings › Research › peer-review

DOI

Recently viewed

Activities

  1. Teaching the machine how to assess grammar skills. Modelling verb-tense exercise characteristics as a basis for an adaptive E-learning system
  2. Plenary lecture eintitled: "Sliding Mode Control Strategies for Maglev Systems based on Kalman Filtering"
  3. Efficacy of an Internet-based problem-solving training for teachers: Results of a randomized controlled trial.
  4. Blogs in the Foreign Language Classroom
  5. Imagining real utopia: An empirical exploration of organizing alternative projects
  6. The global classroom: Introduction, presentation and workshops
  7. Merely Chocolate-Covered Broccoli? From Early CALL to Digital Game-Based Language Learning
  8. Further Creation of Know how in the Opportunity Recognition Process
  9. 131st MLA Annual Convention 2016
  10. It’s all about engagement with texts – Empirical findings about promoting students’ reading comprehension by well-structured texts
  11. Breaks and age related strain in continuous physical work
  12. 23rd (EC)2 Conference - Hypothesis Testing - EC2 2012
  13. Laboratory Tests with various Oxidants.

Publications

  1. Supporting discourse in a synchronous learning environment
  2. How Much Tracking Is Necessary? - The Learning Curve in Bayesian User Journey Analysis
  3. Towards improved dispatching rules for complex shop floor scenarios - A genetic programming approach
  4. Expertise in research integration and implementation for tackling complex problems
  5. Using augmented video to test in-car user experiences of context analog HUDs
  6. Microstructural development of as-cast AM50 during Constrained Friction Processing: grain refinement and influence of process parameters
  7. Special Issue The Discourse of Redundancy Introduction
  8. Multiphase-field modeling of temperature-driven intermetallic compound evolution in an Al-Mg system for application to solid-state joining processes
  9. Failure to Learn From Failure Is Mitigated by Loss-Framing and Corrective Feedback
  10. Towards an open question answering architecture
  11. Systematic feature evaluation for gene name recognition
  12. Guest Editorial - ''Econometrics of Anonymized Micro Data''
  13. Embarrassment as a public vs. private emotion and symbolic coping behaviour
  14. »HOW TO MAKE YOUR OWN SAMPLES«
  15. Meta-Image – a collaborative environment for the image discourse
  16. Development of high performance single-phase solid solution magnesium alloy at low temperature
  17. HAWK - hybrid question answering using linked data
  18. Binary Random Nets II
  19. Is the market classification of risk always efficient?
  20. Reporting and Analysing the Environmental Impact of Language Models on the Example of Commonsense Question Answering with External Knowledge
  21. Does cognitive load moderate the seductive details effect? A multimedia study
  22. Current issues in competence modeling and assessment
  23. Estimation of minimal data sets sizes for machine learning predictions in digital mental health interventions