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We propose a statistical method to estimate simultaneously the non-parametrictransitivity and preferential attachment functions in a growing network, incontrast to conventional methods that either estimate each function inisolation or assume some functional form for them. Our model is shown to be agood fit to two real-world co-authorship networks and be able to bring to lightintriguing details of the preferential attachment and transitivity phenomenathat would be unavailable under traditional methods. We also introduce a methodto quantify the amount of contributions of those phenomena in the growthprocess of a network based on the probabilistic dynamic process induced by themodel formula. Applying this method, we found that transitivity dominated PA inboth co-authorship networks. This suggests the importance of indirect relationsin scientific creative processes. The proposed methods are implemented in the Rpackage FoFaF.

Joint Estimation of the Non-parametric Transitivity and Preferential Attachment Functions in Scientific Co-authorship Networks