Maximizing influence via link prediction in evolving networks
Article
Article Title | Maximizing influence via link prediction in evolving networks |
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Article Category | Article |
Authors | Zhang, Kexin, Li, Mo, Teng, Shuang, Li, Lingling, Wang, Yi, Zhao, Xuezhuan, Di, Jinhong and Zhang, Ji |
Journal Title | Array |
Journal Citation | 24 |
Article Number | 100366 |
Number of Pages | 9 |
Year | 2024 |
Publisher | Elsevier |
Place of Publication | Netherlands |
ISSN | 2590-0056 |
Digital Object Identifier (DOI) | https://doi.org/10.1016/j.array.2024.100366 |
Web Address (URL) | https://www.sciencedirect.com/science/article/pii/S2590005624000328 |
Abstract | Influence Maximization (IM), targeting the optimal selection of đ seed nodes to maximize potential information dissemination in prospectively social networks, garners pivotal interest in diverse realms like viral marketing and political discourse dissemination. Despite receiving substantial scholarly attention, prevailing research predominantly addresses the IM problem within the confines of existing networks, thereby neglecting the dynamic evolutionary character of social networks. An inevitable requisite arises to explore the IM problem in social networks of future contexts, which is imperative for certain application scenarios. In this light, we introduce a novel problem, Influence Maximization in Future Networks (IMFN), aimed at resolving the IM problem within an anticipated future network framework. We establish that the IMFN problem is NP-hard and advocate a prospective solution framework, employing judiciously selected link prediction methods to forecast the future network, and subsequently applying a greedy algorithm to select the đ most influential nodes. Moreover, we present SCOL (Sketch-based Cost-effective lazy forward selection algorithm Optimized with Labeling technique), a well-designed algorithm to accelerate the query of our IMFN problem. Extensive experimental results, rooted in five real-world datasets, are provided, affirming the efficacy and efficiency of the proffered solution and algorithms. |
Keywords | Influence maximization; Future networks; Link prediction; Greedy algorithm; Graph query |
Contains Sensitive Content | Does not contain sensitive content |
ANZSRC Field of Research 2020 | 460299. Artificial intelligence not elsewhere classified |
Byline Affiliations | Zhengzhou University of Aeronautics, China |
Liaoning University, China | |
Henan Yuanfang Human Digital Technology Service Group, China | |
School of Mathematics, Physics and Computing |
https://research.usq.edu.au/item/zv026/maximizing-influence-via-link-prediction-in-evolving-networks
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