Abstract
Influence maximization is an extensively studied optimization problem aiming at finding the best k seed nodes in a network such that they can influence the maximum number of individuals. Traditional heuristic or shortest path based methods either cannot provide any performance guarantee or require huge amount of memory usage, making themselves ineffective in real world applications. In this paper, we propose MSIM: a multi-selector framework which combines the intelligence of different existing algorithms. Our framework consists of three layers: (i) the selector layer; (ii) the combiner layer, and (iii) the evaluator layer. The first layer contains different selectors and each selector can be arbitrary existing influence maximization algorithm. The second layer contains several combiners and combines the output of the first layer in different ways. The third layer evaluates the candidates elected by the second layer to find the best seed nodes in an iterative manner. Experimental results on five real world datasets show that our framework always effectively finds better seed nodes than other state-of-the-art algorithms. Our work provides a new perspective to the study of influence maximization.
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
Notes
- 1.
Our framework is also applicable to undirected or unweighted networks.
- 2.
We call individual algorithms selectors since their main purpose is to select the seed nodes.
- 3.
- 4.
References
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, ACM (2003)
Nemhauser, G.L., Wolsey, L.A., Fisher, M.L.: An analysis of approximations for maximizing submodular set functions-I. Math. Program. 14(1), 265–294 (1978)
Leskovec, J., Krause, A., Guestrin, C., Faloutsos, C., Van-Briesen, J., Glance, N.: Cost-effective outbreak detection in networks. In: Proceedings of the 13th ACM SIGKDD International Conference on Knowledge Discovery and Data Mmining, ACM (2007)
Chen, W., Wang, Y., Yang, S.: Efficient inuence maximization in social networks. In: Proceedings of the 15th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. ACM (2009)
Wang, Y., Feng, X.: A potential-based node selection strategy for influence maximization in a social network. In: Huang, R., Yang, Q., Pei, J., Gama, J., Meng, X., Li, X. (eds.) ADMA 2009. LNCS, vol. 5678, pp. 350–361. Springer, Heidelberg (2009). doi:10.1007/978-3-642-03348-3_34
Kundu, S., Murthy, C.A., Pal, S.K.: A new centrality measure for influence maximization in social networks. In: Kuznetsov, S.O., Mandal, D.P., Kundu, M.K., Pal, S.K. (eds.) PReMI 2011. LNCS, vol. 6744, pp. 242–247. Springer, Heidelberg (2011). doi:10.1007/978-3-642-21786-9_40
Kimura, M., Saito, K.: Tractable models for information diffusion in social networks. In: Fürnkranz, J., Scheffer, T., Spiliopoulou, M. (eds.) PKDD 2006. LNCS, vol. 4213, pp. 259–271. Springer, Heidelberg (2006). doi:10.1007/11871637_27
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, ACM (2010)
Kim, J., Kim, S.-K., Hwanjo, Y.: Scalable and parallelizable processing of influence maximization for large-scale social networks. In: 2013 IEEE 29th International Conference on Data Engineering, IEEE (2013)
Goyal, A., Wei, L., Lakshmanan, L.V.: Celf++: optimizing the greedy algorithm for influence maximization in social networks. In: Proceedings of the 20th International Conference Companion on World Wide Web. ACM (2011)
Shang, J., Zhou, S., Li, X., Liu, L., Hongchun, W.: CoFIM: a community-based framework for influence maximization on large-scale networks. Knowl.-Based Syst. 117, 88–100 (2017)
Chen, W., Collins, A., Cummings, R., et al.: Influence maximization in social networks when negative opinions may emerge and propagate. SDM 11, 379–390 (2011)
Mahajan, V., Muller, E., Bass, F.M.: New product diffusion models in marketing: a review and directions for research. In: Nakićenović, N., Grübler, A. (eds.) Diffusion of Technologies and Social Behavior, pp. 125–177. Springer, Heidelberg (1991). doi:10.1007/978-3-662-02700-4_6
Tang, Y., Shi, Y., Xiao, X.: Influence maximization in near-linear time: a martingale approach. In: Proceedings of the 2015 ACM SIGMOD International Conference on Management of Data, ACM, 1539–1554 (2015)
Tang, Y., Xiao, X., Shi, Y.: Influence maximization: near-optimal time complexity meets practical efficiency. In: Proceedings of the 2014 ACM SIGMOD International Conference on Management of Data, pp. 75–86. ACM (2014)
Acknowledgements
This work was supported by the Fundamental Research Funds for the Central Universities of China (Nos. 106112016CDJXY180003 , 0216001104621).
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2017 Springer International Publishing AG
About this paper
Cite this paper
Shang, J., Wu, H., Zhou, S., Liu, L., Tang, H. (2017). Effective Influence Maximization Based on the Combination of Multiple Selectors. In: Ma, L., Khreishah, A., Zhang, Y., Yan, M. (eds) Wireless Algorithms, Systems, and Applications. WASA 2017. Lecture Notes in Computer Science(), vol 10251. Springer, Cham. https://doi.org/10.1007/978-3-319-60033-8_49
Download citation
DOI: https://doi.org/10.1007/978-3-319-60033-8_49
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-60032-1
Online ISBN: 978-3-319-60033-8
eBook Packages: Computer ScienceComputer Science (R0)