ABEM: An Adaptive Agent-based Evolutionary Approach for Influence Maximization in Dynamic Social Networks
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Li, Weihua
Hu, Yuxuan
Jiang, Chenting
Wu, Shiqing
Bai, Quan
Lai, Edmund
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Elsevier BV
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.
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4605 Data Management and Data Science, 4606 Distributed Computing and Systems Software, 46 Information and Computing Sciences, 4602 Artificial Intelligence, 0102 Applied Mathematics, 0801 Artificial Intelligence and Image Processing, 0806 Information Systems, 4903 Numerical and computational mathematics, Influence maximization, Evolutionary computing, Genetic algorithm, Agent-based modeling
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Applied Soft Computing, ISSN: 1568-4946 (Print), Elsevier BV, 136, 110062. doi: 10.1016/j.asoc.2023.110062
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© 2023 The Author(s). Published by Elsevier B.V. This is an open access article under the CC BY-NC-ND license.
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Except where otherwise noted, this item's license is described as Creative Commons Attribution-NonCommercial-NoDerivatives