An efficient critical node detection algorithm
Article No.: 28, Pages 1 - 4
Abstract
The critical node detection(CND) is to identify one node-set, whose removal can minimize a predefined measure of network connectivity of the residual network. There are significant applications in many fields for CND, however, the CND is still challenging problem. In this study, an efficient algorithm is presented for CND, in which a novel node centrality measure based on network topology is used to estimate node importance and a hybrid heuristics combining local search and weighted random sampling is used to update the critical node set. To validate the proposed algorithm, the experiments are conducted on synthetic and real-world networks, and comparisons are made with existing algorithms. The experimental results illustrate that the proposed algorithm has the excellent performance and outperforms the existing algorithms.
References
[1]
Bernardetta Addis, Roberto Aringhieri, Andrea Grosso, and Pierre Hosteins. 2016. Hybrid constructive heuristics for the critical node problem. Annals of Operations Research 238 (2016), 637–649.
[2]
Roberto Aringhieri, Andrea Grosso, Pierre Hosteins, and Rosario Scatamacchia. 2015. VNS solutions for the critical node problem. Electronic Notes in Discrete Mathematics 47 (2015), 37–44.
[3]
Roberto Aringhieri, Andrea Grosso, Pierre Hosteins, and Rosario Scatamacchia. 2016. Local search metaheuristics for the critical node problem. Networks 67, 3 (2016), 209–221.
[4]
Ashwin Arulselvan, Clayton W. Commander, Lily Elefteriadou, and Panos M. Pardalos. 2009. Detecting critical nodes in sparse graphs. Computers & Operations Research 36, 7 (2009), 2193–2200.
[5]
Duanbing Chen, Linyuan Lü, Ming-Sheng Shang, Yi-Cheng Zhang, and Tao Zhou. 2012. Identifying influential nodes in complex networks. Physica A: Statistical Mechanics and its Applications 391, 4 (2012), 1777–1787.
[6]
Marco Di Summa, Andrea Grosso, and Marco Locatelli. 2012. Branch and cut algorithms for detecting critical nodes in undirected graphs. Computational Optimization and Applications 53 (2012), 649–680.
[7]
Thang N Dinh, Ying Xuan, My T Thai, EK Park, and Taieb Znati. 2010. On approximation of new optimization methods for assessing network vulnerability. In 2010 Proceedings IEEE INFOCOM. IEEE, 1–9.
[8]
Luca Faramondi, Gabriele Oliva, Roberto Setola, Federica Pascucci, Annunziata Esposito Amideo, and Maria Paola Scaparra. 2017. Performance analysis of single and multi-objective approaches for the critical node detection problem. In Optimization and Decision Science: Methodologies and Applications: ODS, Sorrento, Italy, September 4-7, 2017 47. Springer, 315–324.
[9]
Linton C Freeman. 1977. A Set of Measures of Centrality Based on Betweenness. Sociometry 40, 1 (1977), 35–41.
[10]
Pierre Hansen, Nenad Mladenović, and Jose A Moreno Perez. 2010. Variable neighbourhood search: methods and applications. Annals of Operations Research 175 (2010), 367–407.
[11]
F Hooshmand, F Mirarabrazi, and SA MirHassani. 2020. Efficient benders decomposition for distance-based critical node detection problem. Omega 93 (2020), 102037.
[12]
Mohammed Lalou, Mohammed Amin Tahraoui, and Hamamache Kheddouci. 2018. The critical node detection problem in networks: A survey. Computer Science Review 28 (2018), 92–117.
[13]
Helena Ramalhinho Lourenço, Olivier C. Martin, and Thomas Stützle. 2019. Iterated local search: Framework and applications. Springer International Publishing, Cham, 129–168.
[14]
Wayne Pullan. 2015. Heuristic identification of critical nodes in sparse real-world graphs. Journal of Heuristics 21 (2015), 577–598.
[15]
Dalaijargal Purevsuren, Gang Cui, NN Htay Win, and Xiufeng Wang. 2016. Heuristic algorithm for identifying critical nodes in graphs. Advances in Computer Science: an International Journal 5, 3 (2016), 1–4.
[16]
Mario Ventresca. 2012. Global search algorithms using a combinatorial unranking-based problem representation for the critical node detection problem. Computers & Operations Research 39, 11 (2012), 2763–2775.
[17]
Mario Ventresca and Dionne Aleman. 2014. A fast greedy algorithm for the critical node detection problem. In International Conference on Combinatorial Optimization and Applications. Springer, 603–612.
[18]
Alexander Veremyev, Vladimir Boginski, and Eduardo L Pasiliao. 2014. Exact identification of critical nodes in sparse networks via new compact formulations. Optimization Letters 8 (2014), 1245–1259.
[19]
Shanfeng Wang, Maoguo Gong, Wenfeng Liu, and Yue Wu. 2020. Preventing epidemic spreading in networks by community detection and memetic algorithm. Applied Soft Computing 89 (2020), 106118.
[20]
Wen Zhang, Ke-yan Liu, Wanxing Sheng, Songhuai Du, and Dongli Jia. 2020. Critical node identification in active distribution network using resilience and risk theory. IET Generation, Transmission & Distribution 14, 14 (2020), 2771–2778.
[21]
Wengping Zheng, Zhikang Wu, and Gui Yang. 2019. A novel algorithm for identifying critical nodes in networks based on local centrality. Journal of Computer Research and Development 56, 09 (2019), 1872–1880.
[22]
Yangming Zhou, Jin-Kao Hao, Zhang-Hua Fu, Zhe Wang, and Xiangjing Lai. 2020. Variable population memetic search: A case study on the critical node problem. IEEE Transactions on evolutionary computation 25, 1 (2020), 187–200.
Index Terms
- An efficient critical node detection algorithm
Recommendations
A Critical Node Detection Algorithm Based Node Interplay Model
Security, Privacy, and Anonymity in Computation, Communication, and StorageAbstractIdentifying critical nodes helps people to protect these nodes from attacks and prevent these critical nodes from rapidly infecting other nodes in the network with viruses. Gateway Local Rank (GLR) algorithm cannot distinguish the difference ...
Efficient heuristic algorithm for identifying critical nodes in planar networks
Highlights- The critical node problem (CNP) is NP-Hard on planar graphs.
- The objective ...
AbstractThe critical node problem (CNP) removes a limited number of nodes from a network in order to achieve the minimum pair-wise connectivity in the remaining network. In this paper, we present a heuristic algorithm for solving the CNP on ...
Angle Based Critical Nodes Detection (ABCND) for Reliable Industrial Wireless Sensor Networks
AbstractNode failure in the Wireless Sensor Networks (WSN) topology may lead to economic loss, endanger people, and cause environmental damage. Node reliability can be achieved by adequately managing network topology using structural approaches, where the ...
Comments
Information & Contributors
Information
Published In
April 2024
373 pages
ISBN:9798400716607
DOI:10.1145/3663976
Copyright © 2024 ACM.
Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than the author(s) must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected].
Publisher
Association for Computing Machinery
New York, NY, United States
Publication History
Published: 27 June 2024
Check for updates
Author Tags
Qualifiers
- Research-article
- Research
- Refereed limited
Funding Sources
- Ministry of Education Humanities and Social Sciences Research Planning Fund Project
- Foundation of State Key Laboratory of Public Big Data
- Innovation Team Project of Colleges in Guangdong Province
Conference
CVIPPR 2024
CVIPPR 2024: 2024 2nd Asia Conference on Computer Vision, Image Processing and Pattern Recognition
April 26 - 28, 2024
Xiamen, China
Acceptance Rates
Overall Acceptance Rate 14 of 38 submissions, 37%
Contributors
Other Metrics
Bibliometrics & Citations
Bibliometrics
Article Metrics
- 0Total Citations
- 6Total Downloads
- Downloads (Last 12 months)6
- Downloads (Last 6 weeks)2
Reflects downloads up to 17 Oct 2024
Other Metrics
Citations
View Options
Get Access
Login options
Check if you have access through your login credentials or your institution to get full access on this article.
Sign inFull Access
View options
View or Download as a PDF file.
PDFeReader
View online with eReader.
eReaderHTML Format
View this article in HTML Format.
HTML Format