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Poshtiban, Zeinab, Ghanbari, Elham, Jahangir, Mohammadreza. (1402). Identification of Influential Nodes in Social Networks based on Profile Analysis. , 11(4), 535-545. doi: 10.22044/jadm.2023.13276.2457
Zeinab Poshtiban; Elham Ghanbari; Mohammadreza Jahangir. "Identification of Influential Nodes in Social Networks based on Profile Analysis". , 11, 4, 1402, 535-545. doi: 10.22044/jadm.2023.13276.2457
Poshtiban, Zeinab, Ghanbari, Elham, Jahangir, Mohammadreza. (1402). 'Identification of Influential Nodes in Social Networks based on Profile Analysis', , 11(4), pp. 535-545. doi: 10.22044/jadm.2023.13276.2457
Poshtiban, Zeinab, Ghanbari, Elham, Jahangir, Mohammadreza. Identification of Influential Nodes in Social Networks based on Profile Analysis. , 1402; 11(4): 535-545. doi: 10.22044/jadm.2023.13276.2457

Identification of Influential Nodes in Social Networks based on Profile Analysis

Journal of AI and Data Mining
دوره 11، شماره 4، بهمن 2023، صفحه 535-545    اصل مقاله (1.54 M)
نوع مقاله: Methodologies
شناسه دیجیتال (DOI): 10.22044/jadm.2023.13276.2457
نویسندگان
Zeinab Poshtiban؛ Elham Ghanbari* ؛ Mohammadreza Jahangir
Department of Computer Engineering, Yadegar-e-Imam Khomeini (RAH) Shahre Rey Branch, Islamic Azad University, Tehran, Iran.
چکیده
Analyzing the influence of people and nodes in social networks has attracted a lot of attention. Social networks gain meaning, despite the groups, associations, and people interested in a specific issue or topic, and people demonstrate their theoretical and practical tendencies in such places. Influential nodes are often identified based on the information related to the social network structure and less attention is paid to the information spread by the social network user. The present study aims to assess the structural information in the network to identify influential users in addition to using their information in the social network. To this aim, the user’s feelings were extracted. Then, an emotional or affective score was assigned to each user based on an emotional dictionary and his/her weight in the network was determined utilizing centrality criteria. Here, the Twitter network was applied. Thus, the structure of the social network was defined and its graph was drawn after collecting and processing the data. Then, the analysis capability of the network and existing data was extracted and identified based on the algorithm proposed by users and influential nodes. Based on the results, the nodes identified by the proposed algorithm are considered high-quality and the speed of information simulated is higher than other existing algorithms.
کلیدواژه‌ها
Social networks؛ profile analysis؛ emotion retrieval؛ user identification
مراجع
  • Johnson, B. Turnbull, and M. Reisslein, "Social media influence, trust, and conflict: An interview-based study of leadership perceptions", Technology in Society, Vol. 68, No. 101836, 2022.
 

  • N. Dulhare, K. Ahmad, and K.A.B. Ahmad, Machine Learning and Big Data: Concepts, Algorithms, Tools and Applications, John Wiley & sons, 2020.
 

  • Bahutair, A. Zaher, and K. Ibrahim, "NodeRank: Finding influential nodes in social networks based on interests", The Journal of Supercomputing, Vol. 78, No. 2, pp. 2098-2124, 2022.
 

  • K. Jain, S. R. Sahoo, and J. Kaubiyal, "Online social networks security and privacy: comprehensive review and analysis", Complex & Intelligent Systems, Vol. 7, No. 5, pp. 2157-2177, 2021.
 

  • Hajjam and H. Badir, Community detection in social networks In Principles of Social Networking, Springer, 2022.
 

  • Can and B. Alatas, "A new direction in social network analysis: Online social network analysis problems and applications", Physica A: Statistical Mechanics and its Applications, Vol. 535, pp. 122372, 2019.
 

  • C. Bansal, M. Paprzycki, M. Bianchini, and S. Das, Computationally Intelligent Systems and Their Applications, Springer, 2021.
 

  • Rathore, P. K. Sharma, V, Loia, Y. Jeong, and J. H. Park, "Social network security: Issues, challenges, threats, and solutions", Information sciences, Vol. 421, pp. 43-69, 2017.
 

  • Wu, J. Shen, B. Zhou, X. Zhang, and B. Huang, "General link prediction with influential node identification", Physica A: Statistical Mechanics and its Applications, Vol. 523, pp. 996-1007, 2019.
 

  • Zareie, A. Sheikhahmadi, M. Jalili, and M. S. K. Fasaei, "Finding influential nodes in social networks based on neighborhood correlation coefficient", Knowledge-based systems, Vol. 194, pp. 105580, 2020.
 

  • Raamakirtinan and L. M. J Livingston, "Identifying influential users in Facebook-a sentiment-based approach", Indian Journal of Science and technology, Vol. 9, No. 10, pp. 1-9, 2016.
 

  • Deng, L. Xing, L. Zheng, H. Wu, P. Xie, and F. Gao, "A user identification algorithm based on user behavior analysis in social networks", IEEE Access, Vol. 7, pp. 47114-47123, 2019.
 

  • Xiao, D. M. Freeman, and T. Hwa, "Detecting clusters of fake accounts in online social networks", in Proceedings of the 8th ACM Workshop on Artificial Intelligence and Security, pp. 91-101, 2015.
 

  • Liu, J. Wang, H. He, G. Huang, and W. Shi, "Identifying important nodes affecting network security in complex networks", International Journal of Distributed Sensor Networks, Vol. 17, No. 2, pp. 155014772199928 ,2021.
 

  • Heidemann, M. Klier, and F. Probst, "Identifying key users in online social networks: a pagerank based approach", in Proceedings of the IEEE International Conference on Advanced Information Networking and Applications Workshops, pp. 20-13, 2010.
 

  • Kourtellis, T. Alahakoon, R. Simha, A. Iamnitchi, and R. Tripathi, "Identifying high betweenness centrality nodes in large social networks", Social Network Analysis and Mining, Vol. 3, No. 4, pp. 899-914, 2013.
 

  • Sivaganesan, "Novel influence maximization algorithm for social network behavior management", Journal of ISMAC, Vol. 3, No. 01, pp. 60-68, 2021.
 

  • Sovan, V. K. Dubey, and B. Sarkar, "Measure of influences in social networks", Applied Soft Computing, Vol. 99, pp. 106858, 2021.
 

  • Liao and S. Sinthupinyo, "Exclusivity Centrality: A novel centrality measure for finding behavioral influencers on Twitter", in Proceedings of 10th International Conference on Electronics, Computers and Artificial Intelligence, pp. 1-6, 2018.
 

  •  P. D. Meo, M. Levene, F. Messina, and A. Provetti, "A general centrality framework based on node navigability", IEEE Trans. Knowledge and Data Engineering, Vol. 32, No. 11, pp. 2088-2100, 2020.
 

  •  K. Hajarathaiah, M. K. Enduri, S. Anamalamudi, T. S. Rddy, and S. Tokala, "Computing influential nodes using the nearest neighborhood trust value and pagerank in complex networks", Entropy, Vol. 24, No. 5, pp. 704, 2022.
 

  •  A. P. Ogan, P. M. LaCasse, and B. J. Lunday, "Socialnetwork analysis of Twitter interactions: a directed multilayer network approach", Social Network Analysis and Mining, Vol. 13, No.1, pp. 65, 2023.
 

  •  W. Zhang, X. Guo, W. Wang, Q. Tian, L. Pan, and P. Jiao, "Role-based network embedding via structural features reconstruction with degree-regularized constraint", Knowledge-Based Systems, Vol. 218, pp.106872, 2021.
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