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

• Published in: Heliyon Vol. 10; no. 21; p. e40031
• Main Authors: Li, Lingfei, Song, Yingxin, Yang, Wei, Yuan, Kun, Li, Yaguang, Kong, Min, Fathollahi-Fard, Amir M.
• Format: Journal Article
• Language: English
• Published: England Elsevier Ltd 15.11.2024; Elsevier
• Subjects: algorithms; Budgeted influence maximization; Cost model; empirical models; Proxy-based algorithm; seed set; seeds; Social networks; Social networks; Cost model; Budgeted influence maximization; Proxy-based algorithm
• ISSN: 2405-8440; 2405-8440
• DOI: 10.1016/j.heliyon.2024.e40031

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. •Empirical data informs a realistic cost model for individual user activation in social networks.•A novel degree centrality-based algorithm swiftly selects seed nodes for the BIM problem, mitigating overlap.•Experimental validation demonstrates our algorithm's efficiency and superior influence spread compared to alternatives.