research-article

Measuring the Network Vulnerability Based on Markov Criticality

Authors:

Yong ShiAuthors Info & Claims

ACM Transactions on Knowledge Discovery from Data (TKDD), Volume 16, Issue 2

Article No.: 28, Pages 1 - 24

https://doi.org/10.1145/3464390

Published: 21 July 2021 Publication History

Abstract

Vulnerability assessment—a critical issue for networks—attempts to foresee unexpected destructive events or hostile attacks in the whole system. In this article, we consider a new Markov global connectivity metric—Kemeny constant, and take its derivative called Markov criticality to identify critical links. Markov criticality allows us to find links that are most influential on the derivative of Kemeny constant. Thus, we can utilize it to identity a critical link (i, j) from node i to node j, such that removing it leads to a minimization of networks’ global connectivity, i.e., the Kemeny constant. Furthermore, we also define a novel vulnerability index to measure the average speed by which we can disconnect a specified ratio of links with network decomposition. Our method is of high efficiency, which can be easily employed to calculate the Markov criticality in real-life networks. Comprehensive experiments on several synthetic and real-life networks have demonstrated our method’s better performance by comparing it with state-of-the-art baseline approaches.

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Index Terms

Measuring the Network Vulnerability Based on Markov Criticality
1. Mathematics of computing
  1. Discrete mathematics
    1. Graph theory
      1. Graph algorithms

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Published In

cover image ACM Transactions on Knowledge Discovery from Data

ACM Transactions on Knowledge Discovery from Data Volume 16, Issue 2

April 2022

514 pages

ISSN:1556-4681

EISSN:1556-472X

DOI:10.1145/3476120

Editor:
Charu Aggarwal
IBM T. J. Watson Research, USA

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Copyright © 2021 Association for Computing Machinery.

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Publication History

Published: 21 July 2021

Accepted: 01 May 2021

Revised: 01 April 2021

Received: 01 January 2021

Published in TKDD Volume 16, Issue 2

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Fundamental Research Funds for the Central Universities of China
National Natural Science Foundation of China
Beijing Natural Science Foundation
Beijing Natural Science Foundation

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https://doi.org/10.1109/TNSE.2024.3521429
Wu YWang ZLi YGuo YJiang HZhu XWu X(2024)Co-occurrence Order-preserving Pattern Mining with Keypoint Alignment for Time SeriesACM Transactions on Management Information Systems10.1145/365845015:2(1-27)Online publication date: 12-Jun-2024
https://dl.acm.org/doi/10.1145/3658450
Wang YCao JWang YWu JLiu Y(2024)Position Matters: Play a Sequential Game to Detect Significant CommunitiesIEEE Transactions on Knowledge and Data Engineering10.1109/TKDE.2023.332356736:7(3402-3416)Online publication date: Jul-2024
https://doi.org/10.1109/TKDE.2023.3323567
Hao FGao JLin YWu YShang J(2024)Concept Stability Entropy: A Novel Group Cohesion Measure in Social NetworksIEEE Transactions on Emerging Topics in Computing10.1109/TETC.2023.331533512:3(715-726)Online publication date: Jul-2024
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