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
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
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
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
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
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
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
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
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
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
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
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
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
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.
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
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
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
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
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
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
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
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
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
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
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
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.
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
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
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
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
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
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
Identifying influential individuals in social networks: An example of a location-based online social network
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.
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
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
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
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
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
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
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
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
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
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
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
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
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
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.
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
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
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
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
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
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
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
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
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
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
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
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.
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
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
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
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
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
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
There are 32 citations in total.
Details
Primary Language
English
Subjects
Large and Complex Data Theory, Industrial Engineering
Baytur, B., & Özceylan, E. (2024). Identifying influential individuals in social networks: An example of a location-based online social network. Journal of Turkish Operations Management, 8(2), 397-408. https://doi.org/10.56554/jtom.1475874
AMA
Baytur B, Özceylan E. Identifying influential individuals in social networks: An example of a location-based online social network. JTOM. December 2024;8(2):397-408. doi:10.56554/jtom.1475874
Chicago
Baytur, Buşra, and Eren Özceylan. “Identifying Influential Individuals in Social Networks: An Example of a Location-Based Online Social Network”. Journal of Turkish Operations Management 8, no. 2 (December 2024): 397-408. https://doi.org/10.56554/jtom.1475874.
EndNote
Baytur B, Özceylan E (December 1, 2024) Identifying influential individuals in social networks: An example of a location-based online social network. Journal of Turkish Operations Management 8 2 397–408.
IEEE
B. Baytur and E. Özceylan, “Identifying influential individuals in social networks: An example of a location-based online social network”, JTOM, vol. 8, no. 2, pp. 397–408, 2024, doi: 10.56554/jtom.1475874.
ISNAD
Baytur, Buşra - Özceylan, Eren. “Identifying Influential Individuals in Social Networks: An Example of a Location-Based Online Social Network”. Journal of Turkish Operations Management 8/2 (December 2024), 397-408. https://doi.org/10.56554/jtom.1475874.
JAMA
Baytur B, Özceylan E. Identifying influential individuals in social networks: An example of a location-based online social network. JTOM. 2024;8:397–408.
MLA
Baytur, Buşra and Eren Özceylan. “Identifying Influential Individuals in Social Networks: An Example of a Location-Based Online Social Network”. Journal of Turkish Operations Management, vol. 8, no. 2, 2024, pp. 397-08, doi:10.56554/jtom.1475874.
Vancouver
Baytur B, Özceylan E. Identifying influential individuals in social networks: An example of a location-based online social network. JTOM. 2024;8(2):397-408.