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Automated Influence Maintenance in Social Networks: An Agent-based Approach

Citation

Li, W and Bai, Q and Zhang, MJ and Nguyen, TD, Automated Influence Maintenance in Social Networks: An Agent-based Approach, IEEE Transactions on Knowledge and Data Engineering, 31 pp. 1884-1897. ISSN 1041-4347 (2019) [Refereed Article]


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Copyright 2018 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.

DOI: doi:10.1109/TKDE.2018.2867774

Abstract

Social influence modelling and maximization appear significant in various domains, such as e-business, marketing, and social computing. Most existing studies focus on how to maximize positive social impact to promote product adoptions based on static network snapshots. Such approaches can only increase influence in a social network in short-term, but cannot generate sustainable or long-term effects. In this research work, we study how to maintain long-term influence in a social network and propose an agent-based influence maintenance model, which can select influential nodes based on the current status in dynamic social networks in multiple times. Within the context of our investigation, the experimental results indicate that multiple-time seed selection is capable of achieving more constant impact than that of one-shot selection. We claim that influence maintenance is crucial for supporting, enhancing, and assisting long-term goals in business development. The proposed approach can automatically maintain long-lasting impact and achieve influence maintenance.

Item Details

Item Type:Refereed Article
Keywords:influence maintenance, influence diffusion, long-lasting influence, agent-based modelling, multi-agent systems, social network analysis
Research Division:Information and Computing Sciences
Research Group:Artificial intelligence
Research Field:Intelligent robotics
Objective Division:Information and Communication Services
Objective Group:Information systems, technologies and services
Objective Field:Application software packages
UTAS Author:Bai, Q (Dr Quan Bai)
ID Code:138120
Year Published:2019
Web of Science® Times Cited:1
Deposited By:Information and Communication Technology
Deposited On:2020-03-25
Last Modified:2020-08-21
Downloads:1 View Download Statistics

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