[Purpose/significance] The Formation of patent citation is necessary to understand innovation networks. The independence assumption set by the Conventional regression model for observed objects cannot integrate the structural effect factors of the network into the model to provide comprehensive statistical inference. ERGMs (exponential random graph model) represent a methodological innovation of statistical inference for networks given their ability to model actor attributes along with endogenous self-organizational processes and exogenous network covariates.[Method/process] In this paper, ERGMs are applied to systematic inspect the five mechanisms affecting patent citation formation in a sample of Nelarabine drug. The five mechanisms contain main effect, difference effect of citation lag, and activity effect, transitivity effect and network covariates.[Result/conclusion] We find that five different types of mechanisms play diverse roles in patent citation formation. And three of effects among these mechanisms have significant impacts on citation formation of nelarabine drug:network covariates based on shared inventors and shared patent family membership, and transitivity effect. In addition, some aided mechanism play a supporting role on patent citation formation, such as difference of time lag, main effects of number of claims and reference.
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