Identifying influential individuals in social networks: An example of a location-based online social network

Öz

The landscape of information access has evolved significantly over time, with the advent of search engines, social media platforms, and the widespread use of the internet. These developments have fostered a global communication network, resulting in intricate connections between individuals. Online social networks have emerged as key facilitators of social interaction, expediting the exchange of information and playing a pivotal role in content dissemination. Within these networks, certain individuals, termed as Key Players, wield considerable influence, profoundly impacting information diffusion. Thus, the identification of the most influential individuals within complex network structures stands as a crucial challenge. In this study, we employ modularity and eigenvector centrality metrics to designate nodes for initial activation, aiming at influence maximization in social networks. Visualization and analysis of the dataset are conducted using Gephi software, providing insights into the dynamics of the social network structure and facilitating the identification of key players.

Anahtar Kelimeler

• social networks
• influence maximization
• complexity

Kaynakça

1. Acar, S., Gürsoy, H., & Ünsal, N. Ö. 2014. The importance of social business networks for career development in relational society: Sample study of Linkedin. Ejovoc (Electronic Journal of Vocational Colleges). Retrieved from https://dergipark.org.tr/tr/download/article-file/62487
2. Akkaya, M. A. 2021. Perception of internet as an information source and a tool of access to information: Comparison of approach differences between generations. Information Management, 4(2), 222-239. doi: https://doi.org/10.33721/by.947918
3. Arora, A., Galhotra, S., & Ranu, S. 2017. Debunking the myths of influence maximization: An in-depth benchmarking study. In Proceedings of the 2017 ACM international conference on management of data, 651- 666. doi: https://doi.org/10.1145/3035918.3035924
4. Bastian, M., Heymann, S., & Jacomy, M. 2009. Gephi: An open source software for exploring and manipulating networks. In Proceedings of the international AAAI conference on web and social media, 3(1), 361-362. doi: https://doi.org/10.1609/icwsm.v3i1.13937
5. Banerjee, S., Jenamani, M., & Pratihar, D. K. 2020. A survey on influence maximization in a social network. Knowledge and Information Systems, 62, 3417-3455. doi: https://doi.org/10.1007/s10115-020-01461-4
6. Bian, R., Koh, Y. S., Dobbie, G., & Divoli, A. 2019. Identifying top-k nodes in social networks: A survey. ACM Computing Surveys (CSUR), 52(1), 1-33. doi: https://doi.org/10.1145/3301286 Blondel, V. D., Guillaume, J. L., Lambiotte, R., & Lefebvre, E. 2008. Fast unfolding of communities in large networks. Journal of Statistical Mechanics: Theory and Experiment, 2008(10), P10008. doi: https://doi.org/10.1088/1742-5468/2008/10/P10008
7. Boyd, D. M., & Ellison, N. B. 2007. Social network sites: Definition, history, and scholarship. Journal of Computer‐Mediated Communication, 13(1), 210-230. doi: https://doi.org/10.1111/j.1083-6101.2007.00393.x
8. Chen, M., Nguyen, T., & Szymanski, B. K. 2013. On measuring the quality of a network community structure. In 2013 International Conference on Social Computing, IEEE, 122-127. doi: https://doi.org/10.1109/SocialCom.2013.25

9. Codal, K. S., & Coşkun, E. 2016. A network analysis related to the comparison of social network types. AIBU Journal of Social Sciences, 16(1), 143-158. doi: https://doi.org/10.11616/basbed.vi.455794 Das, K., Samanta, S., & Pal, M. 2018. Study on centrality measures in social networks: A survey. Social network analysis and mining, 8, 1-11. doi: https://doi.org/10.1007/s13278-018-0493-2
10. De Salve, A., Mori, P., Guidi, B., Ricci, L., & Pietro, R. D. 2021. Predicting influential users in online social network groups. ACM Transactions on Knowledge Discovery from Data (TKDD), 15(3), 1-50. doi: https://doi.org/10.1145/3441447
11. Domingos, P. & Richardson, M. 2001. Mining the network value of customers. In: Proceedings of the 7th ACM SIGKDD international conference on knowledge discovery and data mining, 57–66. doi: https://doi.org/10.1145/502512.502525
12. Freeman, L. 2004. The development of social network analysis. A Study in the Sociology of Science, 1(687), 159-167. Retrieved from https://www.researchgate.net/profile/Linton-Freeman- 2/publication/239228599_The_Development_of_Social_Network_Analysis/links/54415c650cf2e6f0c0f616a8/T he-Development-of-Social-Network-Analysis.pdf
13. Guille, A., Hacid, H., Favre, C., & Zighed, D. A. 2013. Information diffusion in online social networks: A survey. ACM Sigmod Record, 42(2), 17-28. doi: https://doi.org/10.1145/2503792.2503797 Gursoy, F., & Gunnec, D. 2018. Influence maximization in social networks under deterministic linear threshold model. Knowledge-Based Systems, 161, 111-123. doi: https://doi.org/10.1016/j.knosys.2018.07.040
14. Günneç, D., Raghavan, S., & Zhang, R. 2020. A branch‐and‐cut approach for the least cost influence problem on social networks. Networks, 76(1), 84-105. doi: https://doi.org/10.1002/net.21941 Gürsakal, N. 2009. Social network analysis: Pajek, ucinet and gmine applied. Bursa: Dora Publishing.
15. Jalayer, M., Azheian, M., & Kermani, M. A. M. A. 2018. A hybrid algorithm based on community detection and multi attribute decision making for influence maximization. Computers & Industrial Engineering, 120, 234-250. doi: https://doi.org/10.1016/j.cie.2018.04.049
16. Jaouadi, M., & Romdhane, L. B. 2019. Influence maximization problem in social networks: An overview. In 2019 IEEE/ACS 16th International Conference on Computer Systems and Applications (AICCSA), IEEE, 1-8. doi: https://doi.org/10.1109/AICCSA47632.2019.9035366
17. Kadoić, N., Ređep, N. B., & Divjak, B. 2018. A new method for strategic decision-making in higher education. Central European Journal of Operations Research, 26(3), 611–628. doi: https://doi.org/10.1007/s10100-017- 0497-4 Kazemzadeh, F., Safaei, A. A., Mirzarezaee, M., Afsharian, S., & Kosarirad, H. 2023. Determination of influential nodes based on the Communities’ structure to maximize influence in social networks. Neurocomputing, 534, 18-28. doi: https://doi.org/10.1016/j.neucom.2023.02.059
18. Kempe D., Kleinberg J., Tardos É. 2003. Maximizing the spread of influence through a social network. In: Proceedings of the 9th ACM SIGKDD international conference on knowledge discovery and data mining, 137– 146. doi: https://doi.org/10.1145/956750.956769 Kuikka, V. 2024. Detecting overlapping communities based on ınfluence-spreading matrix and local maxima of a quality function. Computation, 12(4), 85. doi: https://doi.org/10.3390/computation12040085
19. Li, Y., Fan, J., Wang, Y., & Tan, K. L. 2018. Influence maximization on social graphs: A survey. IEEE Transactions on Knowledge and Data Engineering, 30(10), 1852-1872. doi: https://doi.org/10.1109/TKDE.2018.2807843
20. Lü, L., Chen, D., Ren, X. L., Zhang, Q. M., Zhang, Y. C., & Zhou, T. 2016. Vital nodes identification in complex networks. Physics reports, 650, 1-63. doi: https://doi.org/10.1016/j.physrep.2016.06.007
21. Morone, F., & Makse, H. A. 2015. Influence maximization in complex networks through optimal percolation. Nature, 524(7563), 65–68. doi: https://doi.org/10.1038/nature14604
22. O’Malley, A. J. & Marsden, P. V. 2008. The analysis of social networks. Health services and outcomes research methodology, 8, 222-269. doi: https://doi.org/10.1007/s10742-008-0041-z
23. Öztürk, G. 2017. Oral communication to the printing press revolution: Some communication revolutions and society. Turkish Online Journal of Design, Art & Communication, 7(2). doi: https://doi.org/10.7456/10702100/014 Pattanayak, H. S., Saxena, B., & Sinha, A. 2024. Influence maximization in social networks using communitydiversified seed selection. Journal of Complex Networks, 12(1). doi: https://doi.org/10.1093/comnet/cnae008
24. Peng, S., Zhou, Y., Cao, L., Yu, S., Niu, J., & Jia, W. (2018). Influence analysis in social networks: A survey. Journal of Network and Computer Applications, 106, 17-32. doi: https://doi.org/10.1016/j.jnca.2018.01.005
25. Richardson, M. & Domingos, P. 2002. Mining knowledge-sharing sites for viral marketing. In: Proceedings of the 8th ACM SIGKDD international conference on knowledge discovery and data mining, 61–70. doi: https://doi.org/10.1145/775047.775057
26. Saçan, B. C., & Eren, T. (2021). Social media advertising platform selection: An application with multicriteria decision making methods. Journal of Turkish Operations Management, 5(2), 721-738.
27. Sever, N., Humski, L., Ilic, J., Skocir, Z., Pintar, D., & Vranic, M. 2017. Applying the multiclass classification methods for the classification of online social network friends. 25th International Conference on Software, Telecommunications and Computer Networks, SoftCOM 2017. doi: https://doi.org/10.23919/SOFTCOM.2017.8115508 Stanford University. Stanford Network Analysis Project. Access: 29.12.2023, Retrieved from https://snap.stanford.edu/data/loc-Brightkite.html
28. Temizsoy, A., Iori, G., & Montes-Rojas, G. 2017. Network centrality and funding rates in the e-MID interbank market. Journal of Financial Stability, 33, 346-365. doi: https://doi.org/10.1016/j.jfs.2016.11.003
29. Tong, G. A. ., Wu, S. Li, W., & Du, D.-Z. 2016. Effector detection in social networks. IEEE Transactions on Computational Social Systems, 3(4), 151–163. doi: https://doi.org/10.1109/TCSS.2016.2627811
30. Yang, Y., & Pei, J. 2019. Influence analysis in evolving networks: A survey. IEEE Transactions on Knowledge and Data Engineering, 33(3), 1045-1063. doi: https://doi.org/10.1109/TKDE.2019.2934447
31. Zhan, Q., Zhuo, W., & Liu, Y. 2019. Social influence maximization for public health campaigns. IEEE Access, 7, 151252-151260. doi: https://doi.org/10.1109/ACCESS.2019.2946391
32. Zhang, Y., Guo, J., Yang, W., & Wu, W. 2023. Supplementary influence maximization problem in social networks. IEEE Transactions on Computational Social Systems. doi:https://doi.org/10.1109/TCSS.2023.3234437