Abstract
Different measures have been proposed to predict whether individuals will adopt a new behavior in online social networks, given the influence produced by their neighbors. In this paper, we show one can achieve significant improvement over these standard measures, extending them to consider a pair of time constraints. These constraints provide a better proxy for social influence, showing a stronger correlation to the probability of influence as well as the ability to predict influence.
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
Zhang, J., Liu, B., Tang, J., Chen, T., Li, J.: Social influence locality for modeling retweeting behaviors. In: Proceedings of 13th IJCAI 2013, pp. 2761–2767. AAAI Press (2013)
Fink, C., Schmidt, A., Barash, V., Kelly, J., Cameron, C., Macy, M.: Investigating the observability of complex contagion in empirical social networks. In: Proceedings of 10th ICWSM (2016)
Goyal, A., Bonchi, F., Lakshmanan, L.: Learning influence probabilities in social networks. In: 3rd ACM International Conference on Web Search And Data Mining (WSDM 2010), pp. 241–250 (2010)
Kempe, D., Kleinberg, J., Tardos, E.: Maximizing the spread of influence through a social network. In: Proceedings of 9th ACM SIGKDD, pp. 137–146. ACM, New York (2003)
Weng, L., Menczer, F., Ahn, Y.: Virality prediction and community structure in social networks. Sci. Rep. 3 (2013). Article no. 2522
Valente, T.W.: Network Models of the Diffusion of Innovations. Quantitative Methods in Communication. Hampton Press, Cresskill (1995). pp. 153–163
Zafarani, R., Abbasi, M., Liu, H.: Social Media Mining. Cambridge University Press, Cambridge (2014)
Ugander, J., Backstrom, L., Marlow, C., Kleinberg, J.: Structural diversity in social contagion. Proc. Nat. Acad. Sci. 109(16), 5962–5966 (2012)
Attewell, P., David, M., Darren, K.: Data Mining for the Social Sciences. UC Press, Berkeley (2015)
Acknowledgments
Some of the authors of this paper are supported by CNPq-Brazil, AFOSR Young Investigator Program (YIP) grant FA9550-15-1-0159, ARO grant W911NF-15-1-0282, and the DoD Minerva program.
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
Marin, E., Guo, R., Shakarian, P. (2017). Temporal Analysis of Influence to Predict Users’ Adoption in Online Social Networks. In: Lee, D., Lin, YR., Osgood, N., Thomson, R. (eds) Social, Cultural, and Behavioral Modeling. SBP-BRiMS 2017. Lecture Notes in Computer Science(), vol 10354. Springer, Cham. https://doi.org/10.1007/978-3-319-60240-0_31
Download citation
DOI: https://doi.org/10.1007/978-3-319-60240-0_31
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-60239-4
Online ISBN: 978-3-319-60240-0
eBook Packages: Computer ScienceComputer Science (R0)