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Data mining in social network analysis
Zvirinský, Peter ; Mrázová, Iveta (advisor) ; Drotár, Peter (referee) ; Vidnerová, Petra (referee)
Title: Data mining in social network analysis Author: Mgr. Peter Zvirinský Department: Department of Theoretical Computer Science and Mathematical Logic Supervisor: doc. RNDr. Iveta Mrázová CSc., Department of Theoretical Com- puter Science and Mathematical Logic Abstract. In the past several years, the global economy has experienced a sig- nificant increase in overall debt, reaching 238% of the world GDP in 2022, as reported by the International Monetary Fund. This growing indebtedness raises concerns about the stability of the financial system and the welfare of individuals and institutions. It also underscores the need for e ective strategies to under- stand the intricate relationships between debtors and creditors and to mitigate associated risks. In response, this thesis proposes a novel approach based on data mining methods for the comprehensive analysis of debt formation patterns among individuals and companies, focusing on the largely untapped data from the Insolvency Register (IR) of the Czech Republic. We aim to leverage social network analysis (SNA) methods to model and analyze the interactions among subjects participating in insolvencies, namely debtors, creditors, and insolvency administrators. Additionally, we focus our research on dynamic social networks that capture structural changes in...
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