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ABEM: an Adaptive Agent-Based Evolutionary approach for mining influencers in online social networks


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 mining influencers in online social networks, Applied Soft Computing pp. 1-17. ISSN 1568-4946 (2021) [Refereed Article]

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Copyright 2022.Open access under a Creative Commons Attribution 4.0 International (CC BY 4.0) license. (

DOI: doi:10.48550/arXiv.2104.06563


Influence maximization is a crucial optimization problem for predicting the maximum coverage of influence dissemination in real-world social networks. A key step in influence maximization in online social networks is the identification of a small number of users, known as influencers, who are able to spread influence quickly and widely to other users. The evolving nature of the topological structure of these networks makes it difficult to locate and identify these influencers. In this paper, we propose an adaptive agent-based evolutionary approach to address this problem in the context of both static and dynamic networks. This approach is shown to be able to adapt the solution as the network evolves. It 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:2021
Deposited By:Information and Communication Technology
Deposited On:2022-01-25
Last Modified:2022-09-19

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