A new approach to analyzing coevolving longitudinal networks in international relations
Bibliographic Data
| ID | 3918213 |
|---|---|
| Authors | Shahryar Minha (0000-0002-8498-4005, Duke University), Peter D Hoff (0000-0001-8041-0322, Departments of Statistics & Biostatistics, University of Washington), Michael D Ward (0000-0002-6561-6186, Duke University, corresponding author) |
| Year | 2016 |
| Volume | 53 |
| Issue | 3 |
| Pages | 491-505 |
| Publication date | 2016-05-01 |
| 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 Publications Inc (PUBLISHER) |
| DOI | 10.1177/0022343316630783 |
| OpenAlex | W2359252770 |
| Language | EN |
| Citations received | 24 |
| References cited | 63 |
Previous models of international conflict have suffered two shortfalls. They tend not to embody dynamic changes, focusing rather on static slices of behavior over time across a single relational dimension. These models have also been empirically evaluated in ways that assumed the independence of each country, when in reality they are searching for the interdependence among all countries. A number of approaches are available now for analyzing relational data such as international conflict in a network context and a number of these can even handle longitudinal relational data, but none are developed to the point of exploring how networks can coevolve over time. We illustrate a solution to the limitations of existing approaches and apply this novel, dynamic, network based approach to study the dependencies among the ebb and flow of daily international interactions using a newly developed, and openly available, database of events among nations
Context (archaeology) · Data mining · Data science · Dimension (graph theory) · Geography · Independence (probability theory) · Longitudinal data · Point (geometry) · Relational database · Complex Network Analysis Techniques · Computer Science · Mathematics · Opinion Dynamics and Social Influence · Social Capital and Networks · Theoretical Computer Science
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| Unique citing works | 24 |
|---|---|
| Citations per year | 2,4 |
| Citation span | 2016 - 2025 (10) |
| Citation velocity | recent |
| Highly cited | No |
| Citation types | Neutral: 24 |