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International Conference on Machine Learning, Image Processing, Network Security and Data Sciences

MIND 2020: Machine Learning, Image Processing, Network Security and Data Sciences pp 527–542Cite as

An AHP-TOPSIS Based Framework for the Selection of Node Ranking Techniques in Complex Networks

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Part of the book series: Communications in Computer and Information Science ((CCIS,volume 1241))

Abstract

Disparate natural and artificial systems are modelled as complex networks to understand their structural properties and dynamics of phenomena occurring on them. Identification of key components (nodes) of a complex network and ranking them has both theoretical and practical applications. The node ranking techniques are compared on three categories of criteria, namely, Differentiation, Accuracy and Computational Efficiency. Having multiple criteria for technique selection and a number of alternative ranking techniques available renders ranking technique selection in the domain of complex networks as Multi-Criteria Decision Making (MCDM) problem. A number of MCDM methods are accessible in the literature, but no treatment is available for the selection of node ranking techniques based on systematic decision making. In this paper, Analytic Hierarchy Process (AHP), followed by Technique for Order Preference by Similarity to Ideal Solution (TOPSIS), are used in tandem to propose a framework that objectively compares and select node ranking techniques for complex networks. The working of the proposed framework is demonstrated with a dataset of complex networks.

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Authors and Affiliations

  1. UIET, Panjab University, Chandigarh, 160014, India

    Kushal Kanwar, Sakshi Kaushal & Harish Kumar

Authors
  1. Kushal Kanwar
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  2. Sakshi Kaushal
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  3. Harish Kumar
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Corresponding author

Correspondence to Kushal Kanwar .

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Editors and Affiliations

  1. National Institute of Technology Silchar, Silchar, India

    Arup Bhattacharjee

  2. National Institute Of Technology Silchar, Silchar, India

    Samir Kr. Borgohain

  3. National Institute of Technology Silchar, Silchar, India

    Badal Soni

  4. National Institute of Technology Kurukshetra, Kurukshetra, India

    Gyanendra Verma

  5. University of Eastern Finland, Kuopio, Finland

    Xiao-Zhi Gao

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Kanwar, K., Kaushal, S., Kumar, H. (2020). An AHP-TOPSIS Based Framework for the Selection of Node Ranking Techniques in Complex Networks. In: Bhattacharjee, A., Borgohain, S., Soni, B., Verma, G., Gao, XZ. (eds) Machine Learning, Image Processing, Network Security and Data Sciences. MIND 2020. Communications in Computer and Information Science, vol 1241. Springer, Singapore. https://doi.org/10.1007/978-981-15-6318-8_43

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  • DOI: https://doi.org/10.1007/978-981-15-6318-8_43

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  • Publisher Name: Springer, Singapore

  • Print ISBN: 978-981-15-6317-1

  • Online ISBN: 978-981-15-6318-8

  • eBook Packages: Computer ScienceComputer Science (R0)

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