چکیده مقاله
Understanding complex financial systems is crucial for effective regulation and risk management Network analysis offers a powerful tool to uncover hidden patterns in these systems, however, its application to large scale transaction data remains underexplored This paper addresses this gap by applying network analysis techniques to a novel dataset of financial transactions, comprising 4,287 vertices and 27,890 edges A comprehensive evaluation of four prominent community detection algorithms Louvain, Fast Greedy, Infomap, and Label Propagation was conducted These algorithms were assessed based on the network's topological features, with a focus on their performance in the context of financial data This analysis revealed that Louvain and its variants, specifically Louvain and parallel Louvain, outperformed other methods in terms of modularity, conductance, and computational efficiency These algorithms demonstrated superior ability in identifying fine grained community structures, particularly excelling in the initial step of the analysis
کلیدواژهها
نویسندگان
Sara Salimifard
Industrial and Systems Engineering Tarbiat Modares University Tehran, Iran
Babak Teimourpor
Industrial and Systems Engineering Tarbiat Modares University Tehran, Iran
Elham Akhondzadeh Noughabi
Industrial and Systems Engineering Tarbiat Modares University Tehran, Iran
Ruhollah Zeinalipoor
Industrial and Systems Engineering Tarbiat Modares University Tehran, Iran
شیوه ارجاع
Salimifard, Sara and Teimourpor, Babak and Akhondzadeh Noughabi, Elham and Zeinalipoor, Ruhollah,1403,Comparative Analysis of Community Detection Algorithms in Large-Scale Financial Transaction Networks,The 10th International Conference on Industrial and Systems Engineering,Mashhad
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