Relational hyperevent models for the coevolution of coauthoring and citation networks
CoRR(2023)
摘要
The interest in network analysis of bibliographic data has grown
substantially in recent years, yet comprehensive statistical models for
examining the complete dynamics of scientific networks based on bibliographic
data are generally lacking. Current empirical studies often focus on models
restricting analysis either to paper citation networks (paper-by-paper) or
author networks (author-by-author). However, such networks encompass not only
direct connections between papers, but also indirect relationships between the
references of papers connected by a citation link. In this paper, we extend
recently developed relational hyperevent models (RHEM) for analyzing scientific
networks. We introduce new covariates representing theoretically meaningful and
empirically interesting sub-network configurations. The model accommodates
testing hypotheses considering: (i) the polyadic nature of scientific
publication events, and (ii) the interdependencies between authors and
references of current and prior papers. We implement the model using
purpose-built, publicly available open-source software, demonstrating its
empirical value in an analysis of a large publicly available scientific network
dataset. Assessing the relative strength of various effects reveals that both
the hyperedge structure of publication events, as well as the interconnection
between authors and references significantly improve our understanding and
interpretation of collaborative scientific production.
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关键词
relational hyperevent models,networks
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