Influence Maximization in social networks using discretized Harris’ Hawks Optimization algorithm

Khatri, I; Choudhry, A; Rao, A; Tyagi, A; Vishwakarma, DK; Prasad, M

Influence Maximization in social networks using discretized Harris’ Hawks Optimization algorithm

Khatri, I Choudhry, A Rao, A Tyagi, A Vishwakarma, DK Prasad, M

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Publisher:: Elsevier
Publication Type:: Journal Article
Citation:: Applied Soft Computing, 2023, 149, pp. 111037
Issue Date:: 2023-12-01

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Field	Value	Language
dc.contributor.author	Khatri, I
dc.contributor.author	Choudhry, A
dc.contributor.author	Rao, A
dc.contributor.author	Tyagi, A
dc.contributor.author	Vishwakarma, DK
dc.contributor.author	Prasad, M https://orcid.org/0000-0002-7745-9667
dc.date.accessioned	2024-04-11T01:55:02Z
dc.date.available	2024-04-11T01:55:02Z
dc.date.issued	2023-12-01
dc.identifier.citation	Applied Soft Computing, 2023, 149, pp. 111037
dc.identifier.issn	1568-4946
dc.identifier.uri	http://hdl.handle.net/10453/177705
dc.description.abstract	Influence Maximization (IM) is the task of determining k optimal influential nodes in a social network to maximize the influence spread using a propagation model. IM is a prominent problem for viral marketing and helps significantly in social media advertising. Previous Greedy and Reverse Influence Sampling-based IM approaches are ineffective in real-world social networks due to their significant computational cost and execution time. Further, even heuristic approaches applied to IM generally yield minimal performance gain relative to the decreased time complexity. This presents a challenge in developing cost-effective algorithms with low execution time that can handle diverse social networks. In this paper, we propose the discretization of the nature-inspired Harris’ Hawks Optimization meta-heuristic algorithm using community structures for optimal selection of seed nodes for influence spread. In addition to Harris’ Hawks’ intelligence, we employ a neighbor scout strategy algorithm to avoid blindness and enhance the searching ability of the hawks. Further, we use a candidate nodes-based random population initialization approach, and these candidate nodes aid in accelerating the convergence process for the entire populace, reducing the total computational cost. We evaluate the efficacy of our proposed DHHO approach on eight social networks using the Independent Cascade model for information diffusion. We observe that DHHO is comparable or better than competing meta-heuristic approaches for Influence Maximization across five metrics, and performs noticeably better than competing heuristic approaches.
dc.language	en
dc.publisher	Elsevier
dc.relation.ispartof	Applied Soft Computing
dc.relation.isbasedon	10.1016/j.asoc.2023.111037
dc.rights	info:eu-repo/semantics/closedAccess
dc.subject	0102 Applied Mathematics, 0801 Artificial Intelligence and Image Processing, 0806 Information Systems
dc.subject.classification	Artificial Intelligence & Image Processing
dc.subject.classification	4602 Artificial intelligence
dc.subject.classification	4903 Numerical and computational mathematics
dc.title	Influence Maximization in social networks using discretized Harris’ Hawks Optimization algorithm
dc.type	Journal Article
utslib.citation.volume	149
utslib.for	0102 Applied Mathematics
utslib.for	0801 Artificial Intelligence and Image Processing
utslib.for	0806 Information Systems
pubs.organisational-group	University of Technology Sydney
pubs.organisational-group	University of Technology Sydney/Faculty of Engineering and Information Technology
pubs.organisational-group	University of Technology Sydney/Strength - AAII - Australian Artificial Intelligence Institute
pubs.organisational-group	University of Technology Sydney/Faculty of Engineering and Information Technology/School of Computer Science
utslib.copyright.status	closed_access	*
dc.date.updated	2024-04-11T01:54:59Z
pubs.publication-status	Published
pubs.volume	149

Abstract:

Influence Maximization (IM) is the task of determining k optimal influential nodes in a social network to maximize the influence spread using a propagation model. IM is a prominent problem for viral marketing and helps significantly in social media advertising. Previous Greedy and Reverse Influence Sampling-based IM approaches are ineffective in real-world social networks due to their significant computational cost and execution time. Further, even heuristic approaches applied to IM generally yield minimal performance gain relative to the decreased time complexity. This presents a challenge in developing cost-effective algorithms with low execution time that can handle diverse social networks. In this paper, we propose the discretization of the nature-inspired Harris’ Hawks Optimization meta-heuristic algorithm using community structures for optimal selection of seed nodes for influence spread. In addition to Harris’ Hawks’ intelligence, we employ a neighbor scout strategy algorithm to avoid blindness and enhance the searching ability of the hawks. Further, we use a candidate nodes-based random population initialization approach, and these candidate nodes aid in accelerating the convergence process for the entire populace, reducing the total computational cost. We evaluate the efficacy of our proposed DHHO approach on eight social networks using the Independent Cascade model for information diffusion. We observe that DHHO is comparable or better than competing meta-heuristic approaches for Influence Maximization across five metrics, and performs noticeably better than competing heuristic approaches.

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http://hdl.handle.net/10453/177705