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Estimating latent affinity networks with the graphical Lasso

Dados Bibliográficos

ID10151549
AutoresAndrey Tomashevskiy (0000-0002-2940-8398, Political Science, Rutgers University, USA, autor correspondente)
Ano2024
Volume62
Fascículo4
Páginas1112-1127
Data de publicação2024-11-15
Peer ReviewedSim
Open AccessSim
TipoARTICLE
PeriódicoJournal of Peace Research (JOURNAL)
Identificadores do periódicoISSN: 0022-3433 • E-ISSN: 1460-3578
EditoraSAGE Publishing (PUBLISHER • US)
DOI10.1177/00223433241279377
OpenAlexW4404444957
IdiomaEN
Citações recebidas1
Referências citadas44

The notion of affinity among countries is central in studies of international relations: it plays an important role in research as scholars use measures of affinity to study conflict and cooperation in a variety of contexts. To more effectively measure affinity, I argue that it is necessary to utilize multidimensional data and take into account the network context of international relations. In this paper, I develop the deep affinity concept and introduce a new algorithm, the three-step graphical LASSO (GLASSO), to infer and recover latent affinity networks. This technique leverages the abundance of monadic and dyadic state-level data to identify the presence or absence of affinity links between pairs of countries. Directly incorporating network effects and using a variety of multidimensional data inputs, I used the three-step GLASSO to estimate latent affinity links among countries. With these data, I examined the implications of affinity for international conflict and foreign direct investment, and found that the measure of affinity generated with the three-step GLASSO outperformed alternative affinity measures and was associated with decreased conflict and increased economic interaction

Graphical model · Lasso (programming language · Latent variable · Machine learning · Structural equation modeling · World Wide Web · Artificial Intelligence · Bioinformatics and Genomic Networks · Complex Network Analysis Techniques · Computer Science · Mental Health Research Topics · Psychology