Abstract
Given a social network of users represented as a directed graph with edge weight as diffusion probability, selecting a set of highly influential users for initial activation to maximize the influence in the network is popularly known as the Social Influence Maximization Problem. In this paper, we study a different and more practical variant of this problem, where each node is associated with a selection cost which signifies the incentive demand if it is included in the seed set; a fixed budget that can be spent for the seed set selection process; a subset of the nodes designated as the target nodes and each of them is associated with a benefit value that can be earned by influencing the corresponding user; and the goal is to choose a seed set for maximizing the earned benefit within the allocated budget. For this problem, we develop a priority based ranking methodology having three steps. First, marking the effective nodes for the given target nodes; second, priority computation of the effective nodes and the third is to choose the seed nodes based on this priority value within the budget. We implement the proposed methodology with two publicly available social network datasets and observe that the proposed methodology can achieve \(0.03 \text { to } 1.14\) times more benefit compared to the baseline methods without much increase in computational burden.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Algesheimer, R., Dholakia, U.M., Herrmann, A.: The social influence of brand community: evidence from european car clubs. J. Market. 69(3), 19–34 (2005)
Alon, N., Gamzu, I., Tennenholtz, M.: Optimizing budget allocation among channels and influencers. In: Proceedings of the 21st International Conference on World Wide Web, pp. 381–388. ACM (2012)
Aslay, C., Bonchi, F., Lakshmanan, L.V., Lu, W.: Revenue maximization in incentivized social advertising. Proc. VLDB Endowment 10(11), 1238–1249 (2017)
Bakshy, E., Rosenn, I., Marlow, C., Adamic, L.: The role of social networks in information diffusion. In: Proceedings of the 21st International Conference on World Wide Web, pp. 519–528. ACM (2012)
Banerjee, S., Jenamani, M., Pratihar, D.K.: A survey on influence maximization in a social network. arXiv preprint arXiv:1808.05502 (2018)
Chen, W., Wang, C., Wang, Y.: Scalable influence maximization for prevalent viral marketing in large-scale social networks. In: Proceedings of the 16th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 1029–1038. ACM (2010)
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)
Doerr, B., Fouz, M., Friedrich, T.: Why rumors spread so quickly in social networks. Commun. ACM 55(6), 70–75 (2012)
Goel, S., Watts, D.J., Goldstein, D.G.: The structure of online diffusion networks. In: Proceedings of the 13th ACM Conference on Electronic Commerce, pp. 623–638. ACM (2012)
Güney, E.: On the optimal solution of budgeted influence maximization problem in social networks. Oper. Res., 1–15 (2017)
Han, S., Zhuang, F., He, Q., Shi, Z.: Balanced seed selection for budgeted influence maximization in social networks. In: Pacific-Asia Conference on Knowledge Discovery and Data Mining, pp. 65–77. Springer (2014)
Ibrahim, R.A., Hefny, H.A., Hassanien, A.E.: Group impact: local influence maximization in social networks. In: International Conference on Advanced Intelligent Systems and Informatics, pp. 447–455. Springer (2016)
Jung, K., Heo, W., Chen, W.: IRIE: scalable and robust influence maximization in social networks. In: 2012 IEEE 12th International Conference on Data Mining (ICDM), pp. 918–923. IEEE (2012)
Ke, X., Khan, A., Cong, G.: Finding seeds and relevant tags jointly: for targeted influence maximization in social networks. In: Proceedings of the 2018 International Conference on Management of Data, pp. 1097–1111. ACM (2018)
Kempe, D., Kleinberg, J., Tardos, É.: Maximizing the spread of influence through a social network. In: Proceedings of the Ninth ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 137–146. ACM (2003)
Li, Y., Fan, J., Wang, Y., Tan, K.L.: Influence maximization on social graphs: a survey. IEEE Trans. Knowl. Data Eng. (2018)
Narayanam, R., Narahari, Y.: A shapley value-based approach to discover influential nodes in social networks. IEEE Trans. Autom. Sci. Eng. 8(1), 130–147 (2011)
Nguyen, H.T., Thai, M.T., Dinh, T.N.: A billion-scale approximation algorithm for maximizing benefit in viral marketing. IEEE/ACM Trans. Network. (TON) 25(4), 2419–2429 (2017)
Nguyen, H., Zheng, R.: On budgeted influence maximization in social networks. IEEE J. Sel. Areas Commun. 31(6), 1084–1094 (2013)
Wang, C., Chen, W., Wang, Y.: Scalable influence maximization for independent cascade model in large-scale social networks. Data Min. Knowl. Disc. 25(3), 545–576 (2012)
Wang, X., Deng, K., Li, J., Yu, J.X., Jensen, C.S., Yang, X.: Targeted influence minimization in social networks. In: Pacific-Asia Conference on Knowledge Discovery and Data Mining, pp. 689–700. Springer (2018)
Acknowledgment
Authors’ want to thank Ministry of Human Resource and Development (MHRD), Government of India, for sponsoring the project E-business Center of Excellence under the scheme of Center for Training and Research in Frontier Areas of Science and Technology (FAST), Grant No. F.No.5-5/2014-TS.VII.
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2020 Springer Nature Switzerland AG
About this paper
Cite this paper
Banerjee, S., Jenamani, M., Pratihar, D.K., Sirohi, A. (2020). A Priority-Based Ranking Approach for Maximizing the Earned Benefit in an Incentivized Social Network. In: Abraham, A., Cherukuri, A.K., Melin, P., Gandhi, N. (eds) Intelligent Systems Design and Applications. ISDA 2018 2018. Advances in Intelligent Systems and Computing, vol 940. Springer, Cham. https://doi.org/10.1007/978-3-030-16657-1_67
Download citation
DOI: https://doi.org/10.1007/978-3-030-16657-1_67
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-030-16656-4
Online ISBN: 978-3-030-16657-1
eBook Packages: Intelligent Technologies and RoboticsIntelligent Technologies and Robotics (R0)