Abstract
Taking the Foursquare data as an example, this paper investigates the problem of finding influential nodes in a location-based social network (LBSN). In Foursquare, people can share the location they visited and their opinions to others via the actions of checking in and writing tips. These check-ins and tips are likely to influence others on visiting the same places. To study the influence behavior in LBSNs, we first propose the attractiveness model to compute the influence probability among users. Then, we design a one-wave diffusion model, where we focus on the direct impact of the initially selected individuals on their first degree neighbors. Base on these two models, we propose algorithms to select the k influential nodes that maximize the influence spread in the complete-graph network and the network where only the links with friendship are preserved. We empirically show that the k influential nodes selected by our proposed methods have higher influence spread when compared to other methods.
The research in this paper was supported in part by the National Science Council of Taiwan, R.O.C., under Contracts NSC100-2221-E-001-023 and NSC101-2221-E-001-013. All opinions, findings, conclusions and recommendations in this paper are those of the authors and do not necessarily reflect the views of the funding agency.
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Wu, HH., Yeh, MY. (2013). Influential Nodes in a One-Wave Diffusion Model for Location-Based Social Networks. In: Pei, J., Tseng, V.S., Cao, L., Motoda, H., Xu, G. (eds) Advances in Knowledge Discovery and Data Mining. PAKDD 2013. Lecture Notes in Computer Science(), vol 7819. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-37456-2_6
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DOI: https://doi.org/10.1007/978-3-642-37456-2_6
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