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The unified model of social influence and its application in influence maximization

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Abstract

The study of information dissemination on a social network has gained significant importance with the rise of social media. Since the true dynamics are hidden, various diffusion models have been exposed to explain the cascading behavior. Such models require extensive simulation for estimating the dissemination over time. In an earlier work, we proposed a unified model which provides an approximate analytical solution to the problem of predicting probability of infection of every node in the network over time. Our model generalizes a large class of diffusion process. We demonstrate through extensive empirical evaluation that the error of approximation is small. We build upon our unified model to develop an efficient method for influence maximization. Unlike most approaches, we assume that diffusion spreads not only via the edges of the underlying network, but also through temporal functions of external out-of-network processes. We empirically evaluate our approach and compare it against state-of-the-art approaches on real-world large-scale networks. The evaluation demonstrates that our method has significant performance gains over widely used seed-set selection algorithms.

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Notes

  1. Note that this is different from the linear threshold model in Kempe et al. (2003) in that the threshold may change at every time step.

  2. http://digg.com/.

  3. The dataset can be found online at http://www-scf.usc.edu/~ajiteshs/datasets/digg_ASONAM2014.txt (last accessed on Oct 19, 2015).

  4. Density refers to the ratio of the number of links present in the graph to the total number of possible links.

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Acknowledgments

This work is supported by Chevron USA Inc. under the joint project, Center for Interactive Smart Oilfield Technologies (CiSoft), at the University of Southern California.

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Authors and Affiliations

  1. Department of Computer Science, University of Southern California, Los Angeles, USA

    Ajitesh Srivastava

  2. Ming Hsieh Department of Electrical Engineering, University of Southern California, Los Angeles, USA

    Charalampos Chelmis & Viktor K. Prasanna

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  1. Ajitesh Srivastava
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  2. Charalampos Chelmis
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  3. Viktor K. Prasanna
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Correspondence to Ajitesh Srivastava.

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Srivastava, A., Chelmis, C. & Prasanna, V.K. The unified model of social influence and its application in influence maximization. Soc. Netw. Anal. Min. 5, 66 (2015). https://doi.org/10.1007/s13278-015-0305-x

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