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.

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. https://doi.org/10.1126/science.1081058
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. https://doi.org/10.11925/infotech.1003-3513.2010.02.12
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. https://doi.org/10.1016/j.inffus.2022.11.017
4. [4] D. Centola, “The Spread of behavior in an online social network experiment,” Science, Vol.329, No.5996, pp. 1194-1197, 2010. https://doi.org/10.1126/science.1185231
5. [5] S. Aral and D. Walker, “Identifying influential and susceptible members of social networks,” Science, Vol.337, No.6092, pp. 337-341, 2012. https://doi.org/10.1126/science.1215842
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. https://doi.org/10.1016/j.eswa.2018.05.001
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. https://doi.org/10.1016/j.jnca.2017.12.003
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. https://doi.org/10.1016/j.cosrev.2021.100393
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. https://doi.org/10.3969/j.issn.1001-2400.2010.04.011
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. https://doi.org/10.1073/pnas.1510612113
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. https://doi.org/10.1007/s00521-022-07899-7
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. https://doi.org/10.1016/j.ins.2019.08.024
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. https://doi.org/10.1002/int.22723
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. https://doi.org/10.1155/2021/8548639
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. https://doi.org/10.1016/j.future.2018.08.033
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. https://doi.org/10.1016/j.ejor.2019.04.010
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. https://doi.org/10.1016/j.ins.2019.10.061
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. https://doi.org/10.1016/j.knosys.2018.12.003
21. [21] S. Tang and J. Yuan, “Influence maximization with partial feedback,” Operations Research Letters, Vol.48, No.1, pp. 24-28, 2020. https://doi.org/10.1016/j.orl.2019.10.013
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. https://doi.org/10.1016/j.knosys.2019.105265
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. https://doi.org/10.1016/j.physa.2018.08.045
24. [24] V. Batagelj and A. Mrvar, “Pajek datasets.” http://vlado.fmf.uni-lj.si/pub/networks/data/ [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.