Budget-aware local influence iterative algorithm for efficient influence maximization in social networks.

Autor:	Li L; School of Management, Hangzhou Dianzi University, Hangzhou, 310018, China., Song Y; School of Economics and Management, Anhui Normal University, Wuhu, 241000, China., Yang W; School of Management, Hangzhou Dianzi University, Hangzhou, 310018, China., Yuan K; School of Management, Hefei University of Technology, Hefei, 230009, China., Li Y; School of Health Service Management and the Hospital Management Institute, Anhui Medical University, Hefei, 230032, China., Kong M; School of Economics and Management, Anhui Normal University, Wuhu, 241000, China., Fathollahi-Fard AM; Département d'Analytique, Opérations et Technologies de l'Information, Université de Québec à Montreal, 315, Sainte-Catherine Street East, H2X 3X2, Montreal, Canada.; New Era and Development in Civil Engineering Research Group, Scientific Research Center, Al-Ayen University, Nasiriyah, Thi-Qar, 64001, Iraq.
Jazyk:	angličtina
Zdroj:	Heliyon [Heliyon] 2024 Nov 01; Vol. 10 (21), pp. e40031. Date of Electronic Publication: 2024 Nov 01 (Print Publication: 2024).
DOI:	10.1016/j.heliyon.2024.e40031
Abstrakt:	The budgeted influence maximization (BIM) problem aims to identify a set of seed nodes that adhere to predefined budget constraints within a specified network structure and cost model. However, it is difficult for the existing algorithms to achieve a balance between timeliness and effectiveness. To address this challenge, our study initially proposes a refined cost model through empirical scrutiny of Weibo's quote data. Subsequently, we introduce a proxy-based algorithm, i.e., the budget-aware local influence iterative (BLII) algorithm tailored for the BIM problem, aimed at expediently identifying seed nodes. The algorithm approximates the global influence by leveraging the user's one-hop influence and circumvents influence overlap among seed nodes via iterative influence updates. Comparative experiments involving eight algorithms across four real networks demonstrate the effectiveness, efficiency, and robustness of the BLII algorithm. In terms of influence spread, the proposed algorithm outperforms other proxy-based algorithms by 20%-255 % and reaches the state-of-the-art simulation-based approach by 96 %. In addition, the running time of the BLII algorithm is reasonable. Generally, the proposed cost model and BLII algorithm provide novel insights and potent tools for studying BIM problems. Competing Interests: The authors declare the following financial interests/personal relationships which may be considered as potential competing interests: The corresponding author, Prof. Amir M. Fathollahi-Fard, is an Associate Editor in Information Science for Heliyon and was not involved in the editorial review or the decision to publish this article. If there are other authors, they declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper. (© 2024 The Authors. Published by Elsevier Ltd.)
Databáze:	MEDLINE
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