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Detecting influential node in a network using neutrosophic graph and its application
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DOI：10.1007/s00500-023-08234-5.png)
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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.
Keyword:
Neutrosophic graph
Social network
Centrality measure
Influential node
期刊
IF:
2.5
论文数:
1.0W
被引数:
2.1W
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