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Link prediction in a user–object network based on time-weighted resource allocation

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  • Liu, Ji
  • Deng, Guishi

Abstract

Human dynamics has attracted much attention in recent years. Quantitative understanding of the statistical mechanics of human behavior in an online network is a new challenge for researchers. In an online network, users’ behaviors can be abstracted and projected into a user–object network. Many complex problems concerning resource diffusion, such as recommendation system, network flow and social network behavior, can be solved partially by this user–object network. Although some researchers have given some statistical description of the network recently, little work has been done on link prediction in a user–object network. The objective of this paper is to predict new links based on historical ones in a user–object network. When link weight is taken into consideration, we find that both time attenuation and diversion delay play key roles in link prediction in an user–object network. We then combine these two time effect factors of link weight with users’ lifespans and construct the time-weighted network (TWN) model on the basis of resource allocation. Experimental results show the TWN model can greatly enhance the link prediction accuracy.

Suggested Citation

  • Liu, Ji & Deng, Guishi, 2009. "Link prediction in a user–object network based on time-weighted resource allocation," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 388(17), pages 3643-3650.
    • Handle: RePEc:eee:phsmap:v:388:y:2009:i:17:p:3643-3650
    DOI: 10.1016/j.physa.2009.05.021
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    Citations

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    Cited by:

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    2. Kumar, Ajay & Singh, Shashank Sheshar & Singh, Kuldeep & Biswas, Bhaskar, 2020. "Link prediction techniques, applications, and performance: A survey," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 553(C).
    3. Lingling Zhang & Jing Li & Qiuliu Zhang & Fan Meng & Weili Teng, 2019. "Domain Knowledge-Based Link Prediction in Customer-Product Bipartite Graph for Product Recommendation," International Journal of Information Technology & Decision Making (IJITDM), World Scientific Publishing Co. Pte. Ltd., vol. 18(01), pages 311-338, January.
    4. Wang, Yang & Han, Lixin, 2020. "Personalized recommendation via network-based inference with time," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 550(C).
    5. Wen, Yuan & Liu, Yun & Zhang, Zhen-Jiang & Xiong, Fei & Cao, Wei, 2014. "Compare two community-based personalized information recommendation algorithms," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 398(C), pages 199-209.
    6. Kart, Ozge & Ulucay, Oguzhan & Bingol, Berkay & Isik, Zerrin, 2020. "A machine learning-based recommendation model for bipartite networks," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 553(C).
    7. Liu, Chuang & Zhou, Wei-Xing, 2012. "Heterogeneity in initial resource configurations improves a network-based hybrid recommendation algorithm," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 391(22), pages 5704-5711.

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