Extracting Influential Nodes in Social Networks Based On Community Structure

  • Firas Sabar Miften University of Thi-Qar, College of Education for Pure Science, Iraq
  • Zainab Naseem University of Thi-Qar, College of Education for Pure Science, Iraq
  • Evan Abdulkareem Huzam University of Thi-Qar, College of Education for Pure Science, Iraq
Keywords: social networks, community detection, influential nodes, influence propagation, influence maximization.


Influence maximization (IM) is the process focuses on finding active users who make that maximizes the spread of influence into the network. In recent years, community detection has attracted intensive interest especially in the implementation of clustering algorithms in complex networks for community detection. In this paper the social network was divided into communities using the proposed algorithm which is called (CDBNN) algorithm, CDBNN stands for Community Detection Based on Nodes Neighbor. The key nodes (candidate nodes) were extracted using the degree centrality in each community. The propagates model (PSI) was used to information propagates through the network. Finally, using closeness centrality to extract the influential nodes from the network. Experimental results on the real network are efficient for influence propagates, compared with three known proposals.


Koutrouli, Mikaela, et al. "A Guide to Conquer the Biological Network Era Using Graph Theory." Frontiers in Bioengineering and Biotechnology 8 (2020): 34. ‏

Carolina, D. (2012). Finding Influencers in Social Networks. Instituto Superior Tecnico.45

Peng, Sancheng, et al. "Influence analysis in social networks: A survey." Journal of Network and Computer Applications 106 (2018): 17-32. ‏

‏ Zareie, Ahmad, Amir Sheikhahmadi, and Mahdi Jalili. "Identification of influential users in social networks based on users’ interest." Information Sciences 493 (2019): 217-231. ‏

Wang, Fei, et al. "Influence Maximization in Social Network Considering Memory Effect and Social Reinforcement Effect." Future Internet 11.4 (2019): 95. ‏

Wang, Qiyao, et al. "ConformRank: A conformity-based rank for finding top-k influential users." Physica A: Statistical Mechanics and its Applications 474 (2017): 39-48. ‏

KESKİN, MUHAMMED EMRE, and Mehmet Güray Güler. "Influence maximization in social networks: an integer programming approach." Turkish Journal of Electrical Engineering & Computer Sciences 26.6 (2018): 3383-3396. ‏

Jin, Di, et al. "Fast complex network clustering algorithm using local detection." Dianzi Xuebao (Acta Electronica Sinica) 39.11 (2011): 2540-2546. ‏

Al-Falahi, K. G. "Community Detection and Influence Maximization in Online Social Networks ".58 (2014) :100-115.

Farooq, Aftab, et al. "Detection of influential nodes using social networks analysis based on network metrics." International Conference on Computing, Mathematics and Engineering Technologies (iCoMET). IEEE, (2018). ‏

Zhang, Kaiqi, Haifeng Du, and Marcus W. Feldman. "Maximizing influence in a social network: Improved results using a genetic algorithm." Physica A: Statistical Mechanics and its Applications 478 (2017): 20-30. ‏

Tulu, Muluneh Mekonnen, Ronghui Hou, and Talha Younas. "Identifying influential nodes based on community structure to speed up the dissemination of information in complex network." IEEE Access 6 (2018): 7390-7401. ‏

Bozorgi, Arastoo, et al. "Community-based influence maximization in social networks under a competitive linear threshold model." Knowledge-Based Systems 134 (2017): 149-158. ‏

Zhang, Bo, et al. "A most influential node group discovery method for influence maximization in social networks: a trust-based perspective." Data & Knowledge Engineering 121 (2019): 71-87. ‏

Wang, Yunming, et al. "Influential Node Identification in Command and Control Networks Based on Integral k-Shell." Wireless Communications and Mobile Computing 2019 (2019). ‏

Rossi, Maria-Evgenia G., et al. "MATI: An efficient algorithm for influence maximization in social networks." PloS one 13.11 (2018): e0206318. ‏

Zhan, Justin, Sweta Gurung, and Sai Phani Krishna Parsa. "Identification of top-k nodes in large networks using katz centrality." Journal of Big Data 4.1 (2017): 1-19. ‏

Chakraborty, Anwesha, et al. "Application of graph theory in social media." International Journal of Computer Sciences and Engineering 6 (2018): 722-729. ‏

Mao, Chengying, and Weisong Xiao. "A comprehensive algorithm for evaluating node influences in social networks based on preference analysis and random walk." Complexity 2018 (2018). ‏

Golbeck, Jennifer. "Chapter 3—Network Structure and Measures. Analyzing the Social Web." (2013): 25-44. ‏

Kempe, David, Jon Kleinberg, and Éva Tardos. "Maximizing the spread of influence through a social network." Proceedings of the ninth ACM SIGKDD international conference on Knowledge discovery and data mining. (2003). ‏

Han, Jiawei, Jian Pei, and Micheline Kamber. Data mining: concepts and techniques. Elsevier, (2011).‏

Hafiene, Nesrine, and Wafa Karoui. "A new structural and semantic approach for identifying influential nodes in social networks." IEEE/ACS 14th International Conference on Computer Systems and Applications (AICCSA). IEEE, (2017). ‏

Doo, Myungcheol, and Ling Liu. "Probabilistic diffusion of social influence with incentives." IEEE Transactions on Services Computing 7.3 (2014): 387-400. ‏

Song, Guojie, et al. "Influence maximization on large-scale mobile social network: a divide-and-conquer method." IEEE Transactions on Parallel and Distributed Systems 26.5 (2014): 1379-1392. ‏

Hosseini-Pozveh, Maryam, Kamran Zamanifar, and Ahmad Reza Naghsh-Nilchi. "A community-based approach to identify the most influential nodes in social networks." Journal of Information Science 43.2 (2017): 204-220. ‏

Bae, Joonhyun, and Sangwook Kim. "Identifying and ranking influential spreaders in complex networks by neighborhood coreness." Physica A: Statistical Mechanics and its Applications 395 (2014): 549-559. ‏