Abstract
Influence maximization is the problem of finding a set of k users in a social network, such that by targeting these k users one can maximize the spread of influence in the network. Recently a new type of social network has come into existence on platforms like Zomato and Yelp, where people can publish reviews of local businesses like restaurants, hotels, salons etc. Such social network can help owners of local businesses in making intelligent business decisions through the use of Targeted Marketing.
In this paper we present Spread Heuristic based Influence Maximization (SHIM) algorithm, our novel algorithm, which uses a heuristic approach that maximizes the influence spread every time a node is added to the set of influential nodes. In our work, we also introduce a new method to find information-propagation probability based on attributes of the user. We test the proposed algorithm on academic dataset of Yelp, and a comprehensive performance study shows that SHIM algorithm achieves greater Influence Spread than several other algorithms.
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References
Kempe, D., Kleinberg, J., Tardos, E.: Maximizing the Spread of Influence through a social network. In: Proceedings of 9th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (2003)
Richardson, M., Domingos, P.: Mining Knowledge-Sharing Sites for Viral Marketing. In: Eighth International Conference on Knowledge Discovery and Data Mining (2002)
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. ACM (2009)
Kempe, D., Kleinberg, J., Tardos, E.: 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)
Yelp Dataset. https://www.yelp.com/datasetchallenge/dataset
Elliot, N.: Nate Elliott’s Blog. Blogs.forrester.com. N.p., 2015. Web, 14 July 2015
Bass Diffusion Model. Wikipedia. Wikimedia Foundation
Fan, X., Li, V.O.K.: Hierarchy-based algorithm for the influence maximization problem in social networks (2013)
Morris, S.: Contagion. Rev. Econ. Stud. 67(1), 57–78 (2000)
Granovetter, M.: Threshold models of collective behavior. Am. J. Sociol. 83(6), 1420–1443 (1978)
Zhang, H., Dinh, T.N., Thai, T.: Maximizing the spread of positive influence in online social networks. In: International Conference on Distributed Computing Systems
Hoaglin, D.C., Mosteller, F., Tukey, J.W.: Understanding Robust and Exploratory Data Analysis, vol. 3. Wiley, New York (1983)
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Gupta, A., Gupta, T. (2015). SHIM: A Novel Influence Maximization Algorithm for Targeted Marketing. In: Prasath, R., Vuppala, A., Kathirvalavakumar, T. (eds) Mining Intelligence and Knowledge Exploration. MIKE 2015. Lecture Notes in Computer Science(), vol 9468. Springer, Cham. https://doi.org/10.1007/978-3-319-26832-3_31
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DOI: https://doi.org/10.1007/978-3-319-26832-3_31
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