Identifying influential nodes for smart enterprises using community structure with Integrated Feature Ranking

Kumar, Sanjay, Kumar, Akshi ORCID: https://orcid.org/0000-0003-4263-7168 and Panda, BS (2023) Identifying influential nodes for smart enterprises using community structure with Integrated Feature Ranking. IEEE Transactions on Industrial Informatics, 19 (1). pp. 703-711. ISSN 1551-3203

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Official URL: https://ieeexplore.ieee.org/document/9872059

Abstract

Finding influential nodes reshuffles the very notion of linear paths in business processes and replaces it with networks of business value within a smart enterprise system. There are many existing algorithms for identifying influential nodes with certain limitations for applying in large-scale networks. In this paper, we propose a community structure with an Integrated Features Ranking (CIFR) algorithm to find influential nodes in the network. Firstly, we use the community detection algorithm to find communities in the system and then we rank the nodes of network based on three factors, namely- local ranking, gateway ranking, and community ranking, collectively termed as integrated features. Our algorithm intends to select influential nodes, which are both globally and locally optimal, leading to overall high information propagation. We perform the experimental results on total eight networks using various evaluation parameters. The obtained results validate superior performance against contemporary algorithms adding value to smart enterprises.

Item Type:	Article
Peer-reviewed:	Yes
Date Deposited:	13 Sep 2022 08:44
Publisher:	Institute of Electrical and Electronics Engineers (IEEE)
Additional Information:	© 2022 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.
Divisions:	Faculties > Science and Engineering
Subject terms:	08 Information and Computing Sciences, 09 Engineering, 10 Technology, Electrical & Electronic Engineering
URI:	https://e-space.mmu.ac.uk/id/eprint/630334
DOI:	https://doi.org/10.1109/tii.2022.3203059
ISSN	1551-3203
e-ISSN	1941-0050

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