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ABEM: an adaptive agent-based evolutionary approach for influence maximization in dynamic social networks
Citation
Li, W and Hu, Y and Jiang, C and Wu, S and Bai, Q and Lai, E, ABEM: an adaptive agent-based evolutionary approach for influence maximization in dynamic social networks, Applied Soft Computing, 136, (3) Article 110062. ISSN 1568-4946 (2023) [Refereed Article]
Copyright Statement
© 2023 The Author(s). Published by Elsevier B.V. This is an open access article under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International (CC BY-NC-ND 4.0) license, (http://creativecommons.org/licenses/by-nc-nd/4.0/)
DOI: doi:10.1016/j.asoc.2023.110062
Abstract
Influence maximization is recognized as a crucial optimization problem, which aims to identify a limited set of influencers to maximize the coverage of influence dissemination in social networks. However, real-world social networks are usually dynamic and large-scale, which leads to difficulty in capturing real-time user and diffusion features to effectively and accurately select the key influencers. In this paper, we propose an adaptive agent-based evolutionary approach to address this challenging issue with agent-based modeling and genetic algorithm. This novel approach identifies the users’ influence capability in a distributed manner and optimizes the influencer set selection in a dynamic environment. An adaptive solution optimizer is proposed as one of the key components, driving the evolutionary process and adapting the candidate solutions dynamically. The proposed approach is also applicable to large-scale networks due to its distributed framework. Evaluation of our approach is performed by using both synthetic networks and real-world datasets. Experimental results demonstrate that the proposed approach outperforms state-of-the-art seeding algorithms in terms of maximizing influence.
Item Details
| Item Type: | Refereed Article |
|---|---|
| Keywords: | influence maximization, evolutionary computing, agent-based modelling, influence propagation modelling |
| Research Division: | Information and Computing Sciences |
| Research Group: | Artificial intelligence |
| Research Field: | Artificial life and complex adaptive systems |
| Objective Division: | Information and Communication Services |
| Objective Group: | Environmentally sustainable information and communication services |
| Objective Field: | Environmentally sustainable information and communication services not elsewhere classified |
| UTAS Author: | Hu, Y (Miss Yuxuan Hu) |
| UTAS Author: | Jiang, C (Ms Chenting Jiang) |
| UTAS Author: | Wu, S (Mr Shiqing Wu) |
| UTAS Author: | Bai, Q (Dr Quan Bai) |
| ID Code: | 148592 |
| Year Published: | 2023 (online first 2021) |
| Deposited By: | Information and Communication Technology |
| Deposited On: | 2022-01-25 |
| Last Modified: | 2023-03-22 |
| Downloads: | 14 View Download Statistics |
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