Abstract
Viral marketing relies on a set of users who have characteristics to rapidly increase the propagation of information. In viral marketing, information reaches and is shared by the public at large rather than a targeted audience. Thus, the study of influence spread for viral marketing needs the interest of researchers. Influence maximization challenges such as large graph and NP-hard problem have created a need to reconfigure this research issue. The selection of an influential set of users is a well-known influence maximization research problem which is a discrete problem. Therefore, at the beginning of this work, the standard BAT algorithm is transformed into a discrete BAT algorithm (DBAT). Further, a novel discrete BAT-modified (DBATM) algorithm is introduced as an optimized solution to the influence maximization problem. To validate the influence spread performance of the proposed DBATM algorithm, three diverse datasets are experimented using two social network influence maximization techniques—page rank and node rank—and two computational intelligence algorithms—DBAT and DBATM. Experimental outcomes illustrate and posit the fast convergence and maximum influence spread using the computational intelligence optimization algorithm DBATM over all other considered techniques. Even, DBATM performs better than DBAT in terms of the stability as well. Hence, the proposed optimization DBATM algorithm used for the detection of influential users is effective and persuasive for viral marketing.
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Kirti Aggarwal wrote the manuscript under the full guidance and supervision of Dr. Anuja Arora. All authors reviewed the manuscript.
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Aggarwal, K., Arora, A. Influence maximization in social networks using discrete BAT-modified (DBATM) optimization algorithm: a computationally intelligent viral marketing approach. Soc. Netw. Anal. Min. 13, 146 (2023). https://doi.org/10.1007/s13278-023-01151-3
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DOI: https://doi.org/10.1007/s13278-023-01151-3