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Influence maximization in social networks with privacy protection

Author

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  • Zhang, Xian-Jie
  • Wang, Jing
  • Ma, Xiao-Jing
  • Ma, Chuang
  • Kan, Jia-Qian
  • Zhang, Hai-Feng

Abstract

With the explosive development of online social network platforms, how to find a small subset of users (seed nodes) across multiple social networks to maximize the spread of information is of great significance. In reality, different platforms need to consider not only the commercial value of data, but also the protection of data privacy. In this situation, these multiple social platforms can be treated as a system of “multiple private social networks”, which naturally arises a new problem: how to maximize the spread of information in multiple private social networks without breaking the protocol of privacy protection. In view of this, we propose an HE-IM algorithm to solve the problem from the perspective of cryptography. Specifically, we use the homomorphic encryption security protocol and the third-party servers to encrypt and decrypt the influence of nodes in each private network and update the set of seed nodes. The experimental results demonstrate that, by cooperatively fusing information from different private networks in a secret manner, our method can effectively find influential seed nodes to maximize influence in multiple private social networks. The performance of our method in maximizing influence range is much better than that of the baseline methods only considering the structure of single private network. Therefore, the method provides a new way for collaborative search of influential seed nodes in multiple private social networks.

Suggested Citation

  • Zhang, Xian-Jie & Wang, Jing & Ma, Xiao-Jing & Ma, Chuang & Kan, Jia-Qian & Zhang, Hai-Feng, 2022. "Influence maximization in social networks with privacy protection," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 607(C).
    • Handle: RePEc:eee:phsmap:v:607:y:2022:i:c:s0378437122007373
    DOI: 10.1016/j.physa.2022.128179
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    References listed on IDEAS

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    1. Shang, Jiaxing & Wu, Hongchun & Zhou, Shangbo & Zhong, Jiang & Feng, Yong & Qiang, Baohua, 2018. "IMPC: Influence maximization based on multi-neighbor potential in community networks," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 512(C), pages 1085-1103.
    2. Yuanzhi Yang & Lei Yu & Xing Wang & Siyi Chen & You Chen & Yipeng Zhou, 2020. "A novel method to identify influential nodes in complex networks," International Journal of Modern Physics C (IJMPC), World Scientific Publishing Co. Pte. Ltd., vol. 31(02), pages 1-14, February.
    3. Jabari Lotf, Jalil & Abdollahi Azgomi, Mohammad & Ebrahimi Dishabi, Mohammad Reza, 2022. "An improved influence maximization method for social networks based on genetic algorithm," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 586(C).
    4. Wang, Jing & Ma, Xiao-Jing & Xiang, Bing-Bing & Bao, Zhong-Kui & Zhang, Hai-Feng, 2022. "Maximizing influence in social networks by distinguishing the roles of seeds," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 604(C).
    Full references (including those not matched with items on IDEAS)

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