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The contribution of statistical network models to the study of clusters and their evolution
Author(s) -
Hermans Frans
Publication year - 2021
Publication title -
papers in regional science
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.937
H-Index - 64
eISSN - 1435-5957
pISSN - 1056-8190
DOI - 10.1111/pirs.12579
Subject(s) - operationalization , exponential random graph models , field (mathematics) , computer science , economies of agglomeration , management science , econometrics , random graph , graph , data science , theoretical computer science , epistemology , mathematics , economics , microeconomics , philosophy , pure mathematics
Abstract This paper presents a systemic review of the contributions that stochastic actor‐oriented models (SAOMs) and exponential random graph models (ERGMs) have made to the study of industrial clusters and agglomeration processes. Results show that ERGMs and SAOMs are especially popular to study network evolution, proximity dynamics and multiplexity. The paper concludes that although these models have advanced the field by enabling empirical testing of a number of theories, they often operationalize the same theory in completely different ways, making it difficult to draw conclusions that can be generalized beyond the particular case studies on which each paper is based. The paper ends with suggestions of ways to address this problem.