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Fuzzy Influence Graphs

Author

Listed:
  • Sunil Mathew

    (Department of Mathematics, National Institute of Technology, Calicut, Kerala 673601, India)

  • John N. Mordeson

    (Department of Mathematics, Creighton University, Omaha, Nebraska 68178, USA)

Abstract

Trafficking in persons is the most heinous and condemnable organized crime of our time. The eradication of this crime must involve the full cooperation of all. In this paper, we provide a mathematical method to model such cooperation. We use a new concept in fuzzy graph theory, namely, that of incidence. We introduce fuzzy influence graph and characterize influence cutpairs in fuzzy influence graphs since their removal increases the number of connected components of a fuzzy network and thus weakens the potential flow in the network. Some of the properties of influence pairs and influence cutnodes are also studied.

Suggested Citation

  • Sunil Mathew & John N. Mordeson, 2017. "Fuzzy Influence Graphs," New Mathematics and Natural Computation (NMNC), World Scientific Publishing Co. Pte. Ltd., vol. 13(03), pages 311-325, November.
    • Handle: RePEc:wsi:nmncxx:v:13:y:2017:i:03:n:s1793005717400129
    DOI: 10.1142/S1793005717400129
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    References listed on IDEAS

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    1. John N. Mordeson & Sunil Mathew, 2017. "Fuzzy End Nodes in Fuzzy Incidence Graphs," New Mathematics and Natural Computation (NMNC), World Scientific Publishing Co. Pte. Ltd., vol. 13(01), pages 13-20, March.
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    Cited by:

    1. Hafsa Masood Malik & Muhammad Akram & Florentin Smarandache, 2018. "Soft Rough Neutrosophic Influence Graphs with Application," Mathematics, MDPI, vol. 6(7), pages 1-37, July.

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