Abstract
Influence maximization is the problem of finding k seed nodes in a given network as information sources so that the influence cascade can be maximized. To solve this problem both efficiently and effectively, in this paper we propose LAIM: a linear time algorithm for influence maximization in large-scale social networks. Our LAIM algorithm consists of two parts: (1) influence computation; and (2) seed nodes selection. The first part approximates the influence of any node using its local influence, which can be efficiently computed with an iterative algorithm. The second part selects seed nodes in a greedy manner based on the results of the first part. We theoretically prove that the time and space complexities of our algorithm are proportional to the network size. Experimental results on six real-world datasets show that our approach significantly outperforms other state-of-the-art algorithms in terms of influence spread, running time and memory usage.
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Notes
- 1.
Our approach is also applicable to undirected networks.
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References
Domingos, P., Richardson, M.: Mining the network value of customers. In: ACM 7th SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 57–66. ACM, San Francisco (2001)
Goldenberg, J., Libai, B., Muller, E.: Talk of the network: a complex systems look at the underlying process of word-of-mouth. Market. Lett. 12, 211–223 (2001)
Kempe, D., Kleinberg, J., Tardos, É.: Maximizing the spread of influence through a social network. In: ACM 9th SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 137–146. ACM, Washington DC (2003)
Leskovec, J., et al.: Cost-effective outbreak detection in networks. In: ACM 13th SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 420–429. ACM, San Jose (2007)
Goyal, A., Lu, W., Lakshmanan, L.: CELF++: optimizing the greedy algorithm for influence maximization in social networks. In: ACM 20th International Conference Companion on World Wide Web (WWW), pp. 47–48. ACM, Hyderabad (2011)
Chen, W., Wang, Y., Yang, S.: Efficient influence maximization in social networks. In: ACM 15th SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 199–208. ACM, Paris (2009)
Suri, N., Narahari, Y.: Determining the top-k nodes in social networks using the Shapley value. In: ACM 7th International Joint Conference on Autonomous Agents and Multiagent Systems (AAMAS), pp. 1509–1512. ACM, Estoril (2008)
Chen, W., Wang, C., Wang, Y.: Scalable influence maximization for prevalent viral marketing in large-scale social networks. In: ACM 16th SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 1029–1038. ACM, Washington DC (2010)
Kim, J., Kim, S.K., Yu, H.: Scalable and parallelizable processing of influence maximization for large-scale social networks. In: IEEE 29th International Conference on Data Engineering (ICDE), pp. 266–277. IEEE, Brisbane (2013)
Shang, J., et al.: CoFIM: a community-based framework for influence maximization on large-scale networks. Knowl.-Based Syst. 117, 88–100 (2017)
Shang, J., Wu, H., Zhou, S., Liu, L., Tang, H.: Effective influence maximization based on the combination of multiple selectors. In: Ma, L., Khreishah, A., Zhang, Y., Yan, M. (eds.) WASA 2017. LNCS, vol. 10251, pp. 572–583. Springer, Cham (2017). doi:10.1007/978-3-319-60033-8_49
Tang, Y., Xiao, X., Shi, Y.: Influence maximization: near-optimal time complexity meets practical efficiency. In: ACM SIGMOD International Conference on Management of Data, pp. 75–86. ACM, Snowbird (2014)
Tang, Y., Shi, Y., Xiao, X.: Influence maximization in near-linear time: a martingale approach. In: ACM SIGMOD International Conference on Management of Data, pp. 1539–1554. ACM, Melbourne (2015)
Zhang, Q., Huang, C.-C., Xie, J.: Influence spread evaluation and propagation rebuilding. In: Hirose, A., Ozawa, S., Doya, K., Ikeda, K., Lee, M., Liu, D. (eds.) ICONIP 2016. LNCS, vol. 9948, pp. 481–490. Springer, Cham (2016). doi:10.1007/978-3-319-46672-9_54
Acknowledgments
This work was supported in part by National Natural Science Foundation of China (No. 61702059), Graduate Student Research and Innovation Foundation of Chongqing City (No. CYS17024), Fundamental Research Funds for the Central Universities of China (No. 106112016CDJXY180003), China Postdoctoral Science Foundation (No. 2017M612913), Frontier and Application Foundation Research Program of Chongqing City (Nos. cstc2017jcyjAX0340, cstc2015jcyjA40006), Social Undertakings and Livelihood Security Science and Technology Innovation Funds of Chongqing City (No. cstc2017shmsA20013).
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Wu, H., Shang, J., Zhou, S., Feng, Y. (2017). A Linear Time Algorithm for Influence Maximization in Large-Scale Social Networks. In: Liu, D., Xie, S., Li, Y., Zhao, D., El-Alfy, ES. (eds) Neural Information Processing. ICONIP 2017. Lecture Notes in Computer Science(), vol 10638. Springer, Cham. https://doi.org/10.1007/978-3-319-70139-4_76
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