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Integrating Ant Colony Algorithm and Node Centrality to Improve Prediction of Information Diffusion in Social Networks

  • Kasra Majbouri Yazdi
  • Adel Majbouri Yazdi
  • Saeid Khodayi
  • Jingyu Hou
  • Wanlei Zhou
  • Saeed Saedy
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 11342)

Abstract

One of the latest and most important research topics in the field of information diffusion, which has attracted many social network analyst experts in recent years, is how information is disseminated on social networks. In this paper, a new method is proposed by integration of ant colony algorithm and node centrality to increase the prediction accuracy of information diffusion paths on social networks. In the first stage of our approach, centrality of all nodes in the network is calculated. Then, based on the distances of nodes in the network and also ant colony algorithm, the optimal path of propagation is detected. After implementation of the proposed method, 4 real social network data sets were used to evaluate its performance. The evaluation results of all methods showed a better outcome for our method.

Keywords

Information diffusion prediction Information diffusion patterns Ant colony algorithm Node centrality Community detection 

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Copyright information

© Springer Nature Switzerland AG 2018

Authors and Affiliations

  • Kasra Majbouri Yazdi
    • 1
  • Adel Majbouri Yazdi
    • 2
  • Saeid Khodayi
    • 3
  • Jingyu Hou
    • 4
  • Wanlei Zhou
    • 5
  • Saeed Saedy
    • 6
  1. 1.School of Information TechnologyDeakin UniversityMelbourneAustralia
  2. 2.Department of ComputingKharazmi UniversityTehranIran
  3. 3.Faculty of Computer and Electrical EngineeringQazvin Islamic Azad UniversityQazvinIran
  4. 4.School of Information TechnologyDeakin UniversityMelbourneAustralia
  5. 5.School of SoftwareThe University of SydneySydneyAustralia
  6. 6.Faculty of EngineeringKhavaran Higher Education InstituteMashhadIran

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