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Estimating latent affinity networks with the graphical Lasso
Bibliographic Data
| ID | 10151549 |
|---|---|
| Authors | Andrey Tomashevskiy (0000-0002-2940-8398, Political Science, Rutgers University, USA, corresponding author) |
| Year | 2024 |
| Volume | 62 |
| Issue | 4 |
| Pages | 1112-1127 |
| Publication date | 2024-11-15 |
| Peer Reviewed | Yes |
| Open Access | Yes |
| Type | ARTICLE |
| Venue | Journal of Peace Research (JOURNAL) |
| Journal identifiers | ISSN: 0022-3433 • E-ISSN: 1460-3578 |
| Publisher | SAGE Publishing (PUBLISHER • US) |
| DOI | 10.1177/00223433241279377 |
| OpenAlex | W4404444957 |
| Language | EN |
| Citations received | 1 |
| References cited | 44 |
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
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| Unique citing works | 1 |
|---|---|
| Citations per year | 1 |
| Citation span | 2026 - 2026 (1) |
| Citation velocity | current |
| Highly cited | No |
| Citation types | Neutral: 1 |