JACIII Vol.27 No.6 pp. 1037-1044
doi: 10.20965/jaciii.2023.p1037

Research Paper:

Research on the Social Network Search Strategy from the Viewpoint of Comprehensive Influence Maximization

Shumin Hui* and Yuefei Wang**,†

*Library, Taizhou University
No.1139 Shifu Big Road, Jiaojiang District, Taizhou, Zhejiang 318000, China

**Library, Zhejiang Normal University
688 Yingbin Road, Jinhua, Zhejiang 321004, China

Corresponding author

January 25, 2023
March 28, 2023
November 20, 2023
social network, comprehensive influence, strength of influence, searching strategy

Considering that social network provides a channel for nodes to exchange information, resources, and interests, the fundamental task of social network search is to find the best path from the source node to the target node. The search strategy based on the shortest path principle ignores the strength and direction of the social relationship between nodes in the social network, and ignores the difference of influence between nodes, so that the search results cannot meet the needs of searchers. Considering the important role of the influence of nodes and the influence intensity between nodes in social network search, this paper proposes the path optimization principle of maximizing the comprehensive influence, and constructs a new search algorithm based on this strategy by applying the modified Dijkstra algorithm to solve the optimal path between nodes. Using the data of typical real social networks, it is verified that the path optimization algorithm based on the principle of maximizing comprehensive impact is better than the optimization algorithm based on the shortest path, and the search results are better interpretable to users. This paper had proposed a new influence maximization algorithm which has more advantages for solving social network search with high costs or benefits consideration by taking the influence intensity of nodes or between nodes into account.

Cite this article as:
S. Hui and Y. Wang, “Research on the Social Network Search Strategy from the Viewpoint of Comprehensive Influence Maximization,” J. Adv. Comput. Intell. Intell. Inform., Vol.27 No.6, pp. 1037-1044, 2023.
Data files:
  1. [1] P. Dodds, R. Muhamad, and D. Watts, “An experimental study of search in global social networks,” Science, Vol.301, No.5634, pp. 827-830, 2003.
  2. [2] Y. M. Du, G. F. Teng, and J. B. Ma, “Research summary on the key technologies of social network search,” New Technology of Library and Information Service, Vol.26, No.2, pp. 68-73, 2010.
  3. [3] L. Shi, J. Luo, C. Y. Zhu et al., “A survey on cross-media search based on user intention understanding in social networks,” Information Fusion, Vol.91, No.3, pp. 566-581, 2023.
  4. [4] D. Centola, “The Spread of behavior in an online social network experiment,” Science, Vol.329, No.5996, pp. 1194-1197, 2010.
  5. [5] S. Aral and D. Walker, “Identifying influential and susceptible members of social networks,” Science, Vol.337, No.6092, pp. 337-341, 2012.
  6. [6] A. Zareie, A. Sheikhahmadi, and K. Khamforoosh, “Influence maximization in social networks based on TOPSIS,” Expert Systems with Applications, Vol.108, No.15, pp. 96-107, 2018.
  7. [7] S. M. Hui, “Social network searching algorithm based on the strength of influence,” Library and Information Service, Vol.56, No.2, pp. 111-115, 2012.
  8. [8] L. Cui, H. Hu, S. Yu, Q. Yan, Z. Ming, Z. Wen, and N. Lu, “DDSE: A novel evolutionary algorithm based on degree-descending search strategy for influence maximization in social networks,” J. of Network and Computer Applications, Vol.103, pp. 119-130, 2018.
  9. [9] M. Azaouzi, W. Mnasri, and L. B. Romdhane, “New trends in influence maximization models,” Computer Science Review, Vol.40, Article No.100393, 2021.
  10. [10] S. Q. Cai and X. Zhang, “Research on the enterprise’s social network for searching market opportunity information,” Science Research Management, Vol.31, No.1, pp. 126-133, 2010.
  11. [11] Y. F. Sun and L. C. Jiao, “Immune optimization algorithm based on the social network searching model,” J. of Xidian University, Vol.37, No.4, pp. 643-647, 2010.
  12. [12] M. Kearns, A. Roth, Z. S. Wu, and G. Yaroslavtsev, “Private algorithms for the protected in social network search,” Proc. of the National Academy of Sciences of the United States of America, Vol.113, No.4, pp. 913-918, 2016.
  13. [13] K. Z. Zamli, H. S. Alhadawi, and F. Din, “Utilizing the roulette wheel based social network search algorithm for substitution box construction and optimization,” Neural Computing & Applications, Vol.35, No.5, pp. 4051-4071, 2023.
  14. [14] B. Zheng, O. Y. Liu, J. Li et al., “Towards a distributed local-search approach for partitioning large-scale social networks,” Information Sciences, Vol.508, No.1, pp. 200-213, 2020.
  15. [15] L. Shi, J. P. Du, G. Cheng et al., “Cross-media search method based on complementary attention and generative adversarial network for social networks,” Int. J. of Intelligent Systems, Vol.37, No.8, pp. 4393-4416, 2022.
  16. [16] H. Bayzidi, S. Talatahari, M. Saraee, and C. P. Lamarche, “Social network search for solving engineering optimization problems,” Computational Intelligence and Neuroscience, Vol.2021, Article No.8548639, 2021.
  17. [17] C.-W. Tsai and S.-J. Liu, “SEIM: Search economics for influence maximization in online social networks,” Future Generation Computer Systems, Vol.93, pp. 1055-1064, 2019.
  18. [18] K. Tanı nmı s, N. Aras, and I. K. Altı nel, “Influence maximization with deactivation in social networks,” European J. of Operational Research, Vol.278, No.1, pp. 105-119, 2019.
  19. [19] W. Ju, L. Chen, B. Li, W. Liu, J. Sheng, and Y. Wang, “A new algorithm for positive influence maximization in signed networks,” Information Science, Vol.512, pp. 1571-1591, 2020.
  20. [20] Q. Shi, C. Wang, J. Chen, Y. Feng, and C. Chen, “Location driven influence maximization: Online spread via offline deployment,” Knowledge-Based Systems, Vol.166, pp. 30-41, 2019.
  21. [21] S. Tang and J. Yuan, “Influence maximization with partial feedback,” Operations Research Letters, Vol.48, No.1, pp. 24-28, 2020.
  22. [22] J. Ding, W. Sun, J. Wu, and Y. Guo, “Influence maximization based on the realistic independent cascade model,” Knowledge-Based Systems, Vol.191, Article No.105265, 2020.
  23. [23] J. Shang, H. Wu, S. Zhou, J. Zhong, Y. Feng, and B. Qiang, “IMPC: Influence maximization based on multi-neighbor potential in community networks,” Physica A: Statistical Mechanics and its Applications, Vol.512, pp. 1085-1103, 2018.
  24. [24] V. Batagelj and A. Mrvar, “Pajek datasets.” [Accessed May 29, 2021]
  25. [25] A. J. Zhu, “Choosing people and browsing the information—an analysis of the information organization and information access in SinaMicro-Blog,” J. of Intelligence, Vol.30, No.5, pp. 161-164, 2011.

*This site is desgined based on HTML5 and CSS3 for modern browsers, e.g. Chrome, Firefox, Safari, Edge, Opera.

Last updated on Nov. 24, 2023