This chapter explores the application of social network analysis (SNA) as a methodological and conceptual framework for understanding socio-economic interactions in historical contexts. With an emphasis on the interdisciplinary potential of SNA, this chapter examines how network structures influence historical processes, economic behaviour, and institutional developments. The key theoretical foundations, methodological tools, and visual strategies used in historical network analysis are highlighted, with a particular focus on economic history. A diverse range of examples from corporate networks, elite power, financial crisis transmission, and credit access are used to demonstrate how SNA reveals hidden relational dynamics and can be used to reconstruct complex social, financial, and organizational systems. The chapter concludes by addressing current methodological challenges and proposing future directions for integrating big data, computational models, and dynamic analysis into historical research.

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Decoding Complex Socio-Economic Interactions in History Through Social Network Analysis

  • Maria Carmela Schisani,
  • Giancarlo Ragozini

摘要

This chapter explores the application of social network analysis (SNA) as a methodological and conceptual framework for understanding socio-economic interactions in historical contexts. With an emphasis on the interdisciplinary potential of SNA, this chapter examines how network structures influence historical processes, economic behaviour, and institutional developments. The key theoretical foundations, methodological tools, and visual strategies used in historical network analysis are highlighted, with a particular focus on economic history. A diverse range of examples from corporate networks, elite power, financial crisis transmission, and credit access are used to demonstrate how SNA reveals hidden relational dynamics and can be used to reconstruct complex social, financial, and organizational systems. The chapter concludes by addressing current methodological challenges and proposing future directions for integrating big data, computational models, and dynamic analysis into historical research.