A stochastic gradient relational event additive model for modelling US patent citations from 1976 to 2022

被引：2

作者：

Filippi-Mazzola, Edoardo ^{[1
]}

Wit, Ernst C. ^{[1
]}

机构：

[1] Univ Svizzera italiana, Inst Comp, Fac Informat, Via Santa 1, CH-6900 Lugano, Switzerland

来源：

JOURNAL OF THE ROYAL STATISTICAL SOCIETY SERIES C-APPLIED STATISTICS | 2024年 / 73卷 / 04期

基金：

瑞士国家科学基金会;

关键词：

B-splines; citation networks; patent analysis; relational event models; stochastic gradient descent;

D O I：

10.1093/jrsssc/qlae023

中图分类号：

O21 [概率论与数理统计]; C8 [统计学];

学科分类号：

020208 ; 070103 ; 0714 ;

摘要：

Until 2022, the US patent citation network contained almost 10 million patents and over 100 million citations, presenting a challenge in analysing such expansive, intricate networks. To overcome limitations in analysing this complex citation network, we propose a stochastic gradient relational event additive model (STREAM) that models the citation relationships between patents as time events. While the structure of this model relies on the relational event model, STREAM offers a more comprehensive interpretation by modelling the effect of each predictor non-linearly. Overall, our model identifies key factors driving patent citations and reveals insights in the citation process.

引用

页码：1008 / 1024

页数：17