Abstract
The spread of negative influences such as rumors and misinformation in Online Social Networks (OSNs) can threaten public safety. Therefore, the Influence Blocking Maximization (IBM) problem has received extensive attention in recent years. Although many researchers have investigated IBM problem, but there were some issues, including the imbalance between time consumption and performance in seed selection, as well as the influence overlap among selected seed nodes. In this paper, we present an IBM algorithm called IBM-CD based on community division to solve the IBM problem efficiently. This algorithm initially employs our proposed membership propagation approach to divide community centered around source nodes. Subsequently, the communities with low link strength will be merged. Additionally, we use the independent path to calculate the activation probability between node pairs, deriving the blocking effect on nodes based on this concept. Finally, we use the derived blocking effect as the metric to select positive seed nodes for influence blocking within each community. Through experimentation on real world datasets, we demonstrate that our proposed algorithm achieves faster and more effective suppression compared to existing methods.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Similar content being viewed by others
References
Vosoughi, S., Roy, D., Aral, S.: The spread of true and false news online. Science 359(6380), 1146–1151 (2018)
Bharathi, S., Kempe, D., Salek, M.: Competitive influence maximization in social networks. In: Deng, X., Graham, F.C. (eds.) WINE 2007. LNCS, vol. 4858, pp. 306–311. Springer, Heidelberg (2007). https://doi.org/10.1007/978-3-540-77105-0_31
He, X., Song, G., Chen, W.: Influence blocking maximization in social networks under the competitive linear threshold model. In: Proceedings of the 2012 SIAM International Conference on Data Mining, pp. 463–474. SIAM (2012)
Budak, C., Agrawal, D., Abbadi, A.: Limiting the spread of misinformation in social networks. In: Proceedings of the 20th international conference on World Wide Web, pp. 665–674. ACM (2011)
Hosni, AIE., Li, K., Ahmad, S.: Minimizing rumor influence in multiplex online social networks based on human individual and social behaviors. Inform. Sci. 512, 1458–1480 (2020)
Li, Y., Zhu, J., Jiao, J.: Competitive influence minimization in multi-group social networks: an opinion-based solution. IEEE Trans. Network Sci. Eng. 9(4), 2617–2630 (2022)
Chen, W., Wang, Y., Yang, S.: Efficient influence maximization in social networks. In: Proceedings of the 15th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 199–208. ACM (2009)
Manouchehri, M., Helfroush, M., Danyali, H.: Non-uniform influence blocking maximization in social network. Expert Syst. Appl. 207, 118052 (2022)
Yang, L., Li, Z., Giua, A.: Containment of rumor spread in complex social networks. Inf. Sci. 506, 113–130 (2020)
Zhu, W., Yang, W., Xuan, S.: Location-aware influence blocking maximization in social networks. IEEE Access 6, 61462–61477 (2018)
Newman, M., Girvan, M.: Finding and evaluating community structure in networks. Phys. Rev. E 69(2), 026113 (2004)
Blondel, V., Guillaume, J., Lambiotte, R.: Fast unfolding of communities in large networks. J. Stat. Mech: Theory Exp. 2008(10), P10008 (2008)
Ni, Q., Guo, J., Wu, W.: Influence-based community partition with sandwich method for social networks. IEEE Trans. Comput. Soc. Syst. 10(2), 819–830 (2022)
Zhang, Y., Liu, Y., Li, Q.: LILPA: a label importance based label propagation algorithm for community detection with application to core drug discovery. Neurocomputing 413, 107–133 (2020)
Leskovec, J., Huttenlocher, D., Kleinberg, J.: Signed networks in social media. In: Proceedings of the SIGCHI Conference on Human Factors in Computing Systems, pp. 1361–1370. ACM (2010)
Leskovec, J., Kleinberg, J., Faloutsos, C.: Graph evolution: densification and shrinking diameters. ACM Trans. Knowl. Discov. Data (TKDD) 1(1), 2–es (2007)
Acknowledgments
This research was supported in part by the Chinese National Natural Science Foundation (grant number Nos. 61971233, 61702441).
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Ethics declarations
The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.
Rights and permissions
Copyright information
© 2024 The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.
About this paper
Cite this paper
Liu, W., Guo, Z., Chen, L., He, J. (2024). An Influence Blocking Maximization Algorithm Based on Community Division in Social Networks. In: Huang, DS., Chen, W., Zhang, Q. (eds) Advanced Intelligent Computing Technology and Applications. ICIC 2024. Lecture Notes in Computer Science, vol 14874. Springer, Singapore. https://doi.org/10.1007/978-981-97-5618-6_6
Download citation
DOI: https://doi.org/10.1007/978-981-97-5618-6_6
Published:
Publisher Name: Springer, Singapore
Print ISBN: 978-981-97-5617-9
Online ISBN: 978-981-97-5618-6
eBook Packages: Computer ScienceComputer Science (R0)