An emerging area of research is the study of large-scale migration interactions as a network of nodes that represent places (e.g., countries, cities, and rural areas) and edges that encode migration interactions that connect those places. In this chapter, we review interdisciplinary applications of social and spatial network approaches for the analysis of migration networks . We focus in particular on global  migration networks. We describe properties of global migration networks and outline network diagnostics and methods that are relevant to the study of such networks. We then present key findings and propose areas of future research to overcome current challenges. Research on migration networks is multidisciplinary; it connects migration studies to diverse research areas that include sociology, geography, regional science, network science, applied mathematics, computer science, and other areas. Consequently, a major objective of the present chapter is to highlight and foster interdisciplinary conversations .
An emerging area of research is the study of macroscale migration patterns as a network of nodes that represent places (e.g., countries, cities, and rural areas) and edges that encode migration ties that connect those places. In this chapter, we first review advances in the study of migration networks and recent work that has employed network analysis to examine such networks at different geographical scales. In our discussion, we focus in particular on global scale migration networks. We then propose ways to leverage network analysis in concert with digital technologies and online geolocated data to examine the structure and dynamics of migration networks. The implementation of such approaches for studying migration networks faces many challenges, including ethical ones, methodological ones, socio-technological ones (e.g., data availability and reuse), and research reproducibility. We detail these challenges, and we then consider possible ways of linking digital geolocated data to administrative and survey data as a way of harnessing new technologies to construct increasingly realistic migration networks (e.g., using multiplex networks). We also briefly discuss new methods (e.g., multilayer network analysis) in network analysis and adjacent fields (e.g., machine learning) that can help advance understanding of macroscale patterns of migration .
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large-scale migration interactions
macroscale migration patterns
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interactions
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review interdisciplinary applications of social and spatial network approaches for the analysis of migration networks . We
first review advances in the study of migration networks and recent work that has employed network analysis to examine such networks at different geographical scales. In our discussion, we
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scale
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describe properties of global migration networks and outline network diagnostics and methods that are relevant to the study of such networks. We then present key findings and propose areas of future research to overcome current challenges. Research on migration networks is multidisciplinary; it connects migration studies to diverse research areas that include sociology, geography, regional science, network science, applied mathematics, computer science, and other areas. Consequently, a major objective of the present chapter is to highlight and foster interdisciplinary conversations
then propose ways to leverage network analysis in concert with digital technologies and online geolocated data to examine the structure and dynamics of migration networks. The implementation of such approaches for studying migration networks faces many challenges, including ethical ones, methodological ones, socio-technological ones (e.g., data availability and reuse), and research reproducibility. We detail these challenges, and we then consider possible ways of linking digital geolocated data to administrative and survey data as a way of harnessing new technologies to construct increasingly realistic migration networks (e.g., using multiplex networks). We also briefly discuss new methods (e.g., multilayer network analysis) in network analysis and adjacent fields (e.g., machine learning) that can help advance understanding of macroscale patterns of migration