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The Research of “Products Rapidly Attracting Users” Based on the Fully Integrated Link Prediction Algorithm

Author

Listed:
  • Shugang Li

    (School of Management, Shanghai University, Shanghai 200444, China)

  • Ziming Wang

    (School of Management, Shanghai University, Shanghai 200444, China)

  • Beiyan Zhang

    (School of Management, Shanghai University, Shanghai 200444, China)

  • Boyi Zhu

    (School of Management, Shanghai University, Shanghai 200444, China)

  • Zhifang Wen

    (School of Management, Shanghai University, Shanghai 200444, China)

  • Zhaoxu Yu

    (Department of Automation, East China University of Science and Technology, Shanghai 200237, China)

Abstract

One of the main problems encountered by social networks is the cold start problem. The term “cold start problem” refers to the difficulty in predicting new users’ friendships due to the limited number of links those users have with existing nodes. To fill the gap, this paper proposes a Fully Integrated Link Prediction Algorithm (FILPA) that describes the social distance of nodes by using “betweenness centrality,” and develops a Social Distance Index (SDI) based on micro- and macro-network structure according to social distance. With the aim of constructing adaptive SDIs that are suitable for the characteristics of a network, a naive Bayes (NB) method is firstly adopted to select appropriate SDIs according to the density and social distance characteristics of common neighbors in the local network. To avoid the risk of algorithm accuracy reduction caused by blind combination of SDIs, the AdaBoost meta-learning strategy is applied to develop a Fully Integrated Social Distance Index (FISDI) composed of the best SDIs screened by NB. The possible friendships among nodes will then be comprehensively presented using high performance FISDI. Finally, in order to realize the “products rapidly attracting users” in new user marketing, FILPA is used to predict the possible friendship between new users in an online brand community and others in different product circles.

Suggested Citation

  • Shugang Li & Ziming Wang & Beiyan Zhang & Boyi Zhu & Zhifang Wen & Zhaoxu Yu, 2022. "The Research of “Products Rapidly Attracting Users” Based on the Fully Integrated Link Prediction Algorithm," Mathematics, MDPI, vol. 10(14), pages 1-19, July.
    • Handle: RePEc:gam:jmathe:v:10:y:2022:i:14:p:2424-:d:860823
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    References listed on IDEAS

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    1. Alper Ozcan & Sule Gunduz Oguducu, 2019. "Multivariate Time Series Link Prediction for Evolving Heterogeneous Network," International Journal of Information Technology & Decision Making (IJITDM), World Scientific Publishing Co. Pte. Ltd., vol. 18(01), pages 241-286, January.
    2. Yao, Yabing & Zhang, Ruisheng & Yang, Fan & Tang, Jianxin & Yuan, Yongna & Hu, Rongjing, 2018. "Link prediction in complex networks based on the interactions among paths," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 510(C), pages 52-67.
    3. Li, Ji-chao & Zhao, Dan-ling & Ge, Bing-Feng & Yang, Ke-Wei & Chen, Ying-Wu, 2018. "A link prediction method for heterogeneous networks based on BP neural network," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 495(C), pages 1-17.
    4. Lü, Linyuan & Zhou, Tao, 2011. "Link prediction in complex networks: A survey," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 390(6), pages 1150-1170.
    5. Chen, Duanbing & Lü, Linyuan & Shang, Ming-Sheng & Zhang, Yi-Cheng & Zhou, Tao, 2012. "Identifying influential nodes in complex networks," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 391(4), pages 1777-1787.
    6. David Liben‐Nowell & Jon Kleinberg, 2007. "The link‐prediction problem for social networks," Journal of the American Society for Information Science and Technology, Association for Information Science & Technology, vol. 58(7), pages 1019-1031, May.
    7. Rafiee, Samira & Salavati, Chiman & Abdollahpouri, Alireza, 2020. "CNDP: Link prediction based on common neighbors degree penalization," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 539(C).
    8. Wang, Xia & Yu, Chunling & Wei, Yujie, 2012. "Social Media Peer Communication and Impacts on Purchase Intentions: A Consumer Socialization Framework," Journal of Interactive Marketing, Elsevier, vol. 26(4), pages 198-208.
    9. Leo Katz, 1953. "A new status index derived from sociometric analysis," Psychometrika, Springer;The Psychometric Society, vol. 18(1), pages 39-43, March.
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    Cited by:

    1. Shugang Li & He Zhu & Zhifang Wen & Jiayi Li & Yuning Zang & Jiayi Zhang & Ziqian Yan & Yanfang Wei, 2023. "Link Prediction Based on Heterogeneous Social Intimacy and Its Application in Social Influencer Integrated Marketing," Mathematics, MDPI, vol. 11(13), pages 1-19, July.

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