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Joint estimation of non-parametric transitivity and preferential attachment functions in scientific co-authorship networks

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  • Inoue, Masaaki
  • Pham, Thong
  • Shimodaira, Hidetoshi

Abstract

We propose a statistical method for estimating the non-parametric transitivity and preferential attachment functions simultaneously in a growing network, in contrast to conventional methods that either estimate each function in isolation or assume a certain functional form for these. Our model is demonstrated to exhibit a good fit to two real-world co-authorship networks and can illuminate several intriguing details of the preferential attachment and transitivity phenomena that would be unavailable under traditional methods. Moreover, we introduce a method for quantifying the amount of contributions of these phenomena in the growth process of a network based on the probabilistic dynamic process induced by the model formula. By applying this method, we found that transitivity dominated preferential attachment in both co-authorship networks. This suggests the importance of indirect relations in scientific creative processes. The proposed method is implemented in the R package FoFaF.

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  • Inoue, Masaaki & Pham, Thong & Shimodaira, Hidetoshi, 2020. "Joint estimation of non-parametric transitivity and preferential attachment functions in scientific co-authorship networks," Journal of Informetrics, Elsevier, vol. 14(3).
    • Handle: RePEc:eee:infome:v:14:y:2020:i:3:s175115771930269x
    DOI: 10.1016/j.joi.2020.101042
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