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Social Network Analysis and Mining

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01-12-2023 | Original Article

Influence maximization in social networks using discrete BAT-modified (DBATM) optimization algorithm: a computationally intelligent viral marketing approach

Authors: Kirti Aggarwal, Anuja Arora

Published in: Social Network Analysis and Mining | Issue 1/2023

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Abstract

The article introduces a novel approach to influence maximization in social networks using a discrete BAT-modified (DBATM) optimization algorithm. This method aims to identify the most influential nodes that can maximize the spread of information within a social network, addressing the NP-hard problem of influence maximization. The DBATM algorithm is designed to handle large-scale social networks and improve the efficiency of influence maximization. The research is validated through extensive experiments on various datasets, demonstrating the superior performance of the DBATM algorithm compared to traditional methods and other optimization algorithms. The article also highlights the stability and reliability of the DBATM algorithm, making it a promising tool for viral marketing strategies in social media.

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Title: Influence maximization in social networks using discrete BAT-modified (DBATM) optimization algorithm: a computationally intelligent viral marketing approach
Authors: Kirti Aggarwal
Anuja Arora
Publication date: 01-12-2023
Publisher: Springer Vienna
Published in: Social Network Analysis and Mining / Issue 1/2023
Print ISSN: 1869-5450
Electronic ISSN: 1869-5469
DOI: https://doi.org/10.1007/s13278-023-01151-3

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Influence maximization in social networks using discrete BAT-modified (DBATM) optimization algorithm: a computationally intelligent viral marketing approach

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