An Intelligent Centrality Measures for Influential Node Detection in COVID-19 Environment

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Abstract

With an advent of social networks, spamming has posted the most important serious issues among the users. These are termed as influential users who spread the spam messages in the community which has created the social and psychological impact on the users. Hence the identification of such influential nodes has become the most important research challenge. The paper proposes with a method to (1) detect a community using community algorithms with the Laplacian Transition Matrix that is the popular hashtag (2) to find the Influential nodes or users in the Community using Intelligent centrality measure’s (3) The implementation of machine learning algorithm to classify the intensity of users.The extensive experimentations has been carried out using the COVID-19 datasets with the different machine learning algorithms. The methodologies SVM and PCA provide the accuracy of 98.6 than the linear regression for using the new centrality measures and the other scores like NMI, RMS, are found for the methods. As a result finding out the Influential nodes will help us find the Spammy and genuine accounts easily.

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Author and article information

Contributors

J. Jeyasudha:

ORCID: http://orcid.org/0000-0002-2441-6989

jeyasudj@srmist.edu.in

Bio :

J. Jeyasudha

received B.E Degree in Computer Science and Engineering from Bharathidasan University in 2004 and M.E Degree in Computer Science and Engineering from Karunya University in 2006. She is working as Assistant professor in Department of Software Engineering,Faculty of Computing, SRM Institute of Science and Technology, Kattankulathur, Tamilnadu, India. Her research interest includes Machine Learning,Social Networking,Security and Optimization.

G. Usha: ushag@srmist.edu.in

Bio :

Dr. G. Usha

is currently working as an Associate Professor at the software engineering department in SRMIST.She has 11 years of teaching experience.While working in Anna University chennai she worked in research projects for Smart and Secure techniques Research Lab. Her research interest include network security, machine learning, Bio informatics. Dr.G.Usha published nearly 40 research articles in peer reviewed journals and international conferences. She is GATE scorer and awarded as college first rank holder in UG. She is editorial board member for the journal Progress of Electrical and Electronic Engineering. She was awarded as Outstanding Reviewer within Top 10 percentile of reviewers in Elsevier-Pattern Recognition Letters in 2017.She is reviewer of Elsevier Journal - Computer and Electrical Engineering, Elsevier Journal- Pattern Recognition Letters,Springer- Multimedia tools and Applications,IEEE Access. She has coordinated IET sponsored Workshop on Cyber Security ,National Workshop on Internet of Things , National work shop on VANET and its securityIET sponsored National Conference on Big data, cloud and Security. She is a active member of IET, ISTE, Indian Science Congress

Journal

Journal ID (nlm-ta): Wirel Pers Commun

Journal ID (iso-abbrev): Wirel Pers Commun

Title: Wireless Personal Communications

Publisher: Springer US (New York )

ISSN (Print): 0929-6212

ISSN (Electronic): 1572-834X

Publication date (Electronic): 13 May 2021

Pages: 1-27

Affiliations

[1 ]Department of Computer Science and Engineering, SRM Insititute of Science and Technolgy, Chennai, Tamilnadu India

[2 ]Department of Software Engineering, SRM Insititute of Science and Technolgy, Chennai, Tamilnadu India

Author information

J. Jeyasudha http://orcid.org/0000-0002-2441-6989

Article

Publisher ID: 8577

DOI: 10.1007/s11277-021-08577-y

PMC ID: 8118111

SO-VID: 410ec362-e741-4a44-9259-7c9215f94370

License:

This article is made available via the PMC Open Access Subset for unrestricted research re-use and secondary analysis in any form or by any means with acknowledgement of the original source. These permissions are granted for the duration of the World Health Organization (WHO) declaration of COVID-19 as a global pandemic.

History

Date accepted : 4 May 2021

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