research-article

The Manipulability of Centrality Measures-An Axiomatic Approach

Authors:

Tomasz MichalakAuthors Info & Claims

AAMAS '20: Proceedings of the 19th International Conference on Autonomous Agents and MultiAgent Systems

Pages 1467 - 1475

Published: 13 May 2020 Publication History

Abstract

Centrality measures are among the most fundamental tools for social network analysis. Since network data is often incomplete, erroneous, or otherwise manipulated, increasing attention has recently been paid to studying the sensitivity of centrality measures to such distortions. However, thus far no universal method of quantifying the manipulability of centrality measures has been proposed. To bridge this gap in the literature, we take an axiomatic approach. In particular, we introduce a set of intuitive axioms that characterize such a measure, and prove that there exists only one solution that satisfies them. Next, building upon this measure, we quantify the manipulability of the most fundamental centrality measures.

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Cited By

Godziszewski MVorobeychik YMichalak TAgmon NAn BRicci AYeoh W(2023)Adversarial Link Prediction in Spatial NetworksProceedings of the 2023 International Conference on Autonomous Agents and Multiagent Systems10.5555/3545946.3598846(1817-1825)Online publication date: 30-May-2023
https://dl.acm.org/doi/10.5555/3545946.3598846
Waniek MWoźnica JZhou KVorobeychik YMichalak TRahwan T(2023)Hiding From Centrality Measures: A Stackelberg Game PerspectiveIEEE Transactions on Knowledge and Data Engineering10.1109/TKDE.2023.326785435:10(10058-10071)Online publication date: 17-Apr-2023
https://dl.acm.org/doi/10.1109/TKDE.2023.3267854
Waniek MMichalak TWooldridge MRahwan T(2021)How Members of Covert Networks Conceal the Identities of Their LeadersACM Transactions on Intelligent Systems and Technology10.1145/349046213:1(1-29)Online publication date: 29-Nov-2021
https://dl.acm.org/doi/10.1145/3490462

Index Terms

The Manipulability of Centrality Measures-An Axiomatic Approach
1. Information systems
  1. Information retrieval
    1. Retrieval models and ranking
  2. World Wide Web
    1. Web applications
      1. Social networks
2. Security and privacy
  1. Software and application security
    1. Social network security and privacy

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cover image ACM Conferences

AAMAS '20: Proceedings of the 19th International Conference on Autonomous Agents and MultiAgent Systems

May 2020

2289 pages

ISBN:9781450375184

General Chairs:
Amal El Fallah Seghrouchni
Sorbonne University, France
,
Gita Sukthankar
University of Central Florida, United States
,
Program Chairs:
Bo An
Nanyang Technological University, Singapore
,
Neil Yorke-Smith Yorke-Smith
Delft University of Technology, Netherlands

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International Foundation for Autonomous Agents and Multiagent Systems

Richland, SC

Publication History

Published: 13 May 2020

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Polish National Science Center

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AAMAS '19

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SIGAI

AAMAS '19: International Conference on Autonomous Agents and Multiagent Systems

May 9 - 13, 2020

Auckland, New Zealand

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Overall Acceptance Rate 1,155 of 5,036 submissions, 23%

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Cited By

Godziszewski MVorobeychik YMichalak TAgmon NAn BRicci AYeoh W(2023)Adversarial Link Prediction in Spatial NetworksProceedings of the 2023 International Conference on Autonomous Agents and Multiagent Systems10.5555/3545946.3598846(1817-1825)Online publication date: 30-May-2023
https://dl.acm.org/doi/10.5555/3545946.3598846
Waniek MWoźnica JZhou KVorobeychik YMichalak TRahwan T(2023)Hiding From Centrality Measures: A Stackelberg Game PerspectiveIEEE Transactions on Knowledge and Data Engineering10.1109/TKDE.2023.326785435:10(10058-10071)Online publication date: 17-Apr-2023
https://dl.acm.org/doi/10.1109/TKDE.2023.3267854
Waniek MMichalak TWooldridge MRahwan T(2021)How Members of Covert Networks Conceal the Identities of Their LeadersACM Transactions on Intelligent Systems and Technology10.1145/349046213:1(1-29)Online publication date: 29-Nov-2021
https://dl.acm.org/doi/10.1145/3490462

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