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Until 2022, the US patent citation network contained almost 10 millionpatents and over 100 million citations. To overcome limitations in analyzingsuch complex networks, we propose a stochastic gradient relational eventadditive model (STREAM) that models the relationships between citing patents asevents that occur over time, where predictors are modeled through B-splines.Our model identifies key factors driving patent citation and reveals insights,such as time windows where citations are more likely and the relevance of theincreasing citation numbers per patent. Overall, the STREAM offers thepotential for capturing dynamics in large sparse networks while maintaininginterpretability.

A Stochastic Gradient Relational Event Additive Model for modelling US patent citations from 1976 until 2022