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Detecting influential node in a network using neutrosophic graph and its application

  • Fuzzy systems and their mathematics
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Abstract

The identification of a central node in a network is one of the important tasks of social networks. Nowadays, the central node helps grow online businesses, spread news, advertisements, etc. Existing methods for centrality measurement capture the direct reachability of the node. In social networks, parameters such as relationships among the nodes are generally uncertain. This uncertainty can be tracked using either probability theory or fuzzy theory. In this article, the fuzzy theory, particularly the neutrosophic fuzzy theory, is used because, in this concept, more information, such as true values, falsity and indeterminacy, is incorporated. Thus, the representation of social networks using neutrosophic graphs gives more information compared to fuzzy graphs. This study introduces a new form of centrality measurement using a neutrosophic graph. This measurement considers the different merits of individuals in a network. Individual merits (self-weight) have been included in the proposed method. A small network of university faculty members has been considered to illustrate the problem and to demonstrate the potential fields of application of this new method of centrality measurement.

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Authors and Affiliations

  1. Department of Applied Mathematics with Oceanology and Computer Programming, Vidyasagar University, Midnapur, West Bengal, 721102, India

    Rupkumar Mahapatra & Madhumangal Pal

  2. Department of Mathematics, Tamralipta Mahavidyalaya, Tamluk, West Bengal, 721636, India

    Sovan Samanta

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  1. Rupkumar Mahapatra
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  2. Sovan Samanta
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  3. Madhumangal Pal
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Correspondence to Sovan Samanta.

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Mahapatra, R., Samanta, S. & Pal, M. Detecting influential node in a network using neutrosophic graph and its application. Soft Comput 27, 9247–9260 (2023). https://doi.org/10.1007/s00500-023-08234-5

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