Abstract
With the growth of social networks, significant amount of data is brought online that can benefit applications of many kinds if being effectively utilized. As a typical example, Domnigos proposed the concept of viral marketing, which uses the “word of mouth” marketing technique over virtual networks (Domingos, IEEE Intell Syst 20:80–82, 2005). Each user is associated with a network value that represents his/her influence in the network. The network value is used along with other intrinsic features that represent user shopping behaviors for the selection of a small subset of most influential users in the network for marketing purpose. However, most existing viral marketing techniques ignore the dynamic nature of the virtual network where both the features and the relationship of users may change over time. In this paper, we develop a novel framework for the selection of users by exploiting the temporal dynamics of the network. Incorporating temporal dynamics of the network would assist in selecting an optimal subset of users with the maximum influence over the network. This paper focuses on developing an algorithm for the selection of the users to market the product by exploiting the temporal and the structural dynamics of the network. Extensive experimental results over real-world datasets clearly demonstrate the effectiveness of the proposed framework.
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
References
Domingos P (2005) Mining social networks for viral marketing. IEEE Intell Syst 20:80–82
Merkle (2010) View from the social inbox 2010. http://www.merkleinc.com/user-assets/Documents/WhitePapers/Social%20Inbox%202010%20WPaper%20Final.pdf
Gen Y (2009) Study shows Gen Y wants more control in email exchanges
Epsilon (2008) Asia Pacific consumer email survey. Technical report
Chakrabarti D, Kumar R, Tomkins A (2006) Evolutionary clustering. In: Proceedings of the 12th ACM SIGKDD international conference on knowledge discovery and data mining, pp 554–560
Kempe D, Kleinberg J, Tardos V (2003) Maximizing the spread of influence through a social network. In: International conference on knowledge discovery and data mining, pp 137–146
Xu X, Long B, Zhang Z, Yu PS (2007) Community learning by graph approximation. In: Proceedings of the seventh IEEE international conference on data mining, pp 232–241
Xiang T, Gong S (2008) Spectral clustering with eigenvector selection. Pattern Recognit 41:1012–1029
Hopcroft J, Tarjan R (1973) Efficient algorithms for graph manipulation. Commun ACM 16:372–378
Michael F, David HC, Boris I (1989) Some implementations of the boxplot. Am Stat 43:50–54
Celli F, Di Lascio FML, Magnani M, Pacelli B, Rossi L (2010) Social network data and practices: the case of friendfeed. In: International conference on social computing, behavioral modeling and prediction, Berlin (2010)
Hep-th—kdl—umass amherst. https://kdl.cs.umass.edu/download/attachments/3440884/hepth-schema.png?version=1&modificationDate=1345733950033
Massa PAP (2006) Trust-aware bootstrapping of recommender systems. In: Proceedings of ECAI 2006 workshop on recommender systems, pp 29–33
Richardson M, Domingos P (2002) Mining knowledge-sharing sites for viral marketing. In: Eighth international conference on knowledge discovery and data mining, pp 61–70
Domingos P, Richardson M (2001) Mining the network value of customers. In: Seventh international conference on knowledge discovery and data mining, pp 57–66
Aalst WMvd, Song M (2004) Mining social networks: uncovering interaction patterns in business processes. In: Desel J, Pernici B, Weske M (eds) Business process management. Springer, Berlin, pp 244–260
Leskovec J, Adamic LA, Huberman BA (2007) The dynamics of viral marketing. ACM Trans Web (TWEB) 1:5-228–5-237
Long B, Xu X, Yu PS, Zhang Z (2007) Community learning by graph approximation. In: Proceedings of the 2007 seventh IEEE international conference on data mining, pp 232–241
Chi Y, Zhu S, Song X, Tatemura J, Tseng BL (2007) Structural and temporal analysis of the blogosphere through community factorization. In: Proceedings of the 13th ACM SIGKDD international conference on knowledge discovery and data mining, pp 163–172
Sharan U, Neville J (2007) Exploiting time-varying relationships in statistical relational models. In: Proceedings of the 9th WebKDD and 1st SNA-KDD 2007 workshop on web mining and social network analysis, pp 9–15
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2015 Springer International Publishing Switzerland
About this chapter
Cite this chapter
Naik, S.A., Yu, Q. (2015). Evolutionary Influence Maximization in Viral Marketing. In: Ulusoy, Ö., Tansel, A., Arkun, E. (eds) Recommendation and Search in Social Networks. Lecture Notes in Social Networks. Springer, Cham. https://doi.org/10.1007/978-3-319-14379-8_11
Download citation
DOI: https://doi.org/10.1007/978-3-319-14379-8_11
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-14378-1
Online ISBN: 978-3-319-14379-8
eBook Packages: Computer ScienceComputer Science (R0)