[Paper Review] Network analysis of international migration

Fuad Aleskerov, Natalia Meshcheryakova+2 more|arXiv (Cornell University)|Jun 15, 2018

Complex Network Analysis Techniques16 references4 citations

TL;DR

This paper applies network analysis to international migration flows from 1970 to 2013, introducing new centrality indices—SRIC (short-range indirect centrality) and LRIC (long-range indirect centrality)—that incorporate indirect migration flows and destination country population. It identifies the most influential countries in the global migration network, showing that classical and new centrality measures consistently highlight major migration hubs and highly interconnected nations.

ABSTRACT

The paper analyses international migration flows from the network perspective by the evaluation of centrality indices. In order to find the most influential countries in the international migration network classical centrality indices and new centrality indices are evaluated. New centrality indices consider short (SRIC) and long-range (LRIC) indirect interactions and the node attribute: population of the destination country. The model is applied to the annual data on international migration flows from 1970 to 2013 provided by United Nations Organization. The analysis is made for one year of each decade and indices dynamics is described. It is shown that countries with huge migration flows are outlined by both classical and SRIC, LRIC indices, and SRIC and LRIC indices point out countries with considerable outflows of migrants to countries highly involved in international migration and the most interconnected countries.

Motivation & Objective

• To analyze international migration as a complex network to identify influential countries beyond direct flow measures.
• To address the limitation of classical centrality measures by incorporating indirect migration flows and destination population.
• To evaluate the dynamic evolution of migration network structure across decades (1970s, 1980s, 1990s, 2000s).
• To develop and apply new centrality indices—SRIC and LRIC—that account for short- and long-range indirect interactions.
• To provide a more nuanced understanding of migration influence by integrating node attributes such as destination population.

Proposed method

• Construct a directed weighted network where nodes represent countries and edges represent annual international migration flows from 1970 to 2013.
• Apply classical centrality measures (degree, betweenness, eigenvector) to assess node influence.
• Introduce SRIC and LRIC indices that quantify indirect influence through short- and long-range migration pathways.
• Incorporate the population of the destination country as a node attribute to weight indirect flows in SRIC and LRIC calculations.
• Use annual data from the United Nations to analyze one representative year per decade (1970, 1980, 1990, 2000).
• Compare classical and new centrality indices to identify consistent and novel patterns in migration network influence.

Experimental results

Research questions

• RQ1 Which countries emerge as the most influential in the international migration network when using classical and new centrality measures?
• RQ2 How do SRIC and LRIC indices differ from classical centrality in identifying key migration hubs?
• RQ3 To what extent do indirect migration flows and destination country population shape the perceived influence of a nation in the migration network?
• RQ4 How has the structure and key players in the global migration network evolved from 1970 to 2013?
• RQ5 Do countries with high outflows to highly connected destinations show elevated centrality in SRIC and LRIC measures?

Key findings

• Countries with high migration inflows and outflows—such as the United States, Russia, Germany, and Saudi Arabia—consistently rank highly across classical and new centrality indices.
• SRIC and LRIC indices highlight nations that are not only major sources or destinations but also serve as critical intermediaries in indirect migration pathways.
• The inclusion of destination country population in centrality measures amplifies the influence of large, destination-rich countries like the U.S. and Germany.
• The dynamics of centrality indices show that migration network structure evolved significantly over time, with shifts in dominance among major countries.
• SRIC and LRIC reveal hidden structural roles: countries with substantial outflows to highly connected destinations gain elevated centrality, indicating their role in shaping indirect migration flows.
• The model demonstrates that classical centrality measures alone may underestimate the influence of countries that facilitate long-range migration chains.