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Nonparametric Estimation of the Preferential Attachment Function in Complex Networks: Evidence of Deviations from Log Linearity

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Proceedings of ECCS 2014

Part of the book series: Springer Proceedings in Complexity ((SPCOM))

Abstract

We introduce a statistically sound method called PAFit for the joint estimation of preferential attachment and node fitness in temporal complex networks. Together these mechanisms play a crucial role in shaping network topology by governing the way in which nodes acquire new edges over time. PAFit is an advance over previous methods in so far as it does not make any assumptions on the functional form of the preferential attachment function. We found that the application of PAFit to a publicly available Flickr social network dataset turned up clear evidence for a deviation of the preferential attachment function from the popularly assumed log-linear form. What is more, we were surprised to find that hubs are not always the nodes with the highest node fitnesses. PAFit is implemented in an R package of the same name.

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Acknowledgments

This work was supported by grants from Japan Society for the Promotion of Science KAKENHI (Grant numbers 26120523 and 24300106 to Hidetoshi Shimodaira).

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Authors and Affiliations

  1. Osaka University, Osaka, Japan

    Thong Pham & Hidetoshi Shimodaira

  2. The University of Tokyo, Tokyo, Japan

    Paul Sheridan

Authors
  1. Thong Pham
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  2. Paul Sheridan
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  3. Hidetoshi Shimodaira
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Corresponding author

Correspondence to Thong Pham .

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Editors and Affiliations

  1. RAF, University of Zürich, Zürich, Switzerland

    Stefano Battiston

  2. CREATE-NET, Trento, Italy

    Francesco De Pellegrini

  3. IMT Lucca, Lucca, Italy

    Guido Caldarelli

  4. School of Science and Technology, University of Camerino, Camerino, Italy

    Emanuela Merelli

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Pham, T., Sheridan, P., Shimodaira, H. (2016). Nonparametric Estimation of the Preferential Attachment Function in Complex Networks: Evidence of Deviations from Log Linearity. In: Battiston, S., De Pellegrini, F., Caldarelli, G., Merelli, E. (eds) Proceedings of ECCS 2014. Springer Proceedings in Complexity. Springer, Cham. https://doi.org/10.1007/978-3-319-29228-1_13

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