A Network-Based Analysis of International Refugee Migration Patterns Using GERGMs
Date of Original Version
Understanding determinants of migration is central to anticipating and mitigating the adverse effects of large-scale human displacement. Traditional migration models quantify the influence of different factors on migration but fail to consider the interdependent nature of human displacement. In contrast, network models inherently take into account interdependencies in data, making them ideal for modeling relational phenomena such as migration. In this study, we apply one such model, a Generalized Exponential Random Graph Model (GERGM), to two different weighted-edge networks of international refugee migration from 2015, centered around Syria and the Democratic Republic of Congo (DRC), respectively. The GERGM quantifies the influence of various factors on out-migration and in-migration within the networks, allowing us to determine which push and pull factors are largely at play. Our results indicate that both push factors and pull factors drive migration within the DRC network, while migration within the Syria network is predominately driven by push factors. We suspect the reason for this difference may lie in that the conflict in Syria is relatively recent, in contrast to the conflict in the DRC, which has been ongoing for almost two decades, allowing for the establishment of systematic migration channels, migration networks, and resettlement, all which are related to pull factors, throughout the years.
Publication Title, e.g., Journal
Studies in Computational Intelligence
Abramski, Katherine, Natallia Katenka, and Marc Hutchison. "A Network-Based Analysis of International Refugee Migration Patterns Using GERGMs." Studies in Computational Intelligence 882 SCI, (2020): 387-400. doi: 10.1007/978-3-030-36683-4_32.