{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,10,12]],"date-time":"2025-10-12T03:02:25Z","timestamp":1760238145409,"version":"build-2065373602"},"reference-count":48,"publisher":"MDPI AG","issue":"7","license":[{"start":{"date-parts":[[2022,7,20]],"date-time":"2022-07-20T00:00:00Z","timestamp":1658275200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"Ministry of Education, Science, and Technological Development of the Republic of Serbia"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Entropy"],"abstract":"<jats:p>Various mathematical frameworks play an essential role in understanding the economic systems and the emergence of crises in them. Understanding the relation between the structure of connections between the system\u2019s constituents and the emergence of a crisis is of great importance. In this paper, we propose a novel method for the inference of economic systems\u2019 structures based on complex networks theory utilizing the time series of prices. Our network is obtained from the correlation matrix between the time series of companies\u2019 prices by imposing a threshold on the values of the correlation coefficients. The optimal value of the threshold is determined by comparing the spectral properties of the threshold network and the correlation matrix. We analyze the community structure of the obtained networks and the relation between communities\u2019 inter and intra-connectivity as indicators of systemic risk. Our results show how an economic system\u2019s behavior is related to its structure and how the crisis is reflected in changes in the structure. We show how regulation and deregulation affect the structure of the system. We demonstrate that our method can identify high systemic risks and measure the impact of the actions taken to increase the system\u2019s stability.<\/jats:p>","DOI":"10.3390\/e24071005","type":"journal-article","created":{"date-parts":[[2022,7,20]],"date-time":"2022-07-20T11:22:24Z","timestamp":1658316144000},"page":"1005","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Evolution of Cohesion between USA Financial Sector Companies before, during, and Post-Economic Crisis: Complex Networks Approach"],"prefix":"10.3390","volume":"24","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-9188-5614","authenticated-orcid":false,"given":"Vojin","family":"Stevi\u0107","sequence":"first","affiliation":[{"name":"University of Belgrade-School of Electrical Engineering, Bulevar Kralja Aleksandra 73, 11120 Belgrade, Serbia"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-1212-0803","authenticated-orcid":false,"given":"Marija","family":"Ra\u0161ajski","sequence":"additional","affiliation":[{"name":"University of Belgrade-School of Electrical Engineering, Bulevar Kralja Aleksandra 73, 11120 Belgrade, Serbia"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-8484-3501","authenticated-orcid":false,"given":"Marija","family":"Mitrovi\u0107 Dankulov","sequence":"additional","affiliation":[{"name":"Institute of Physics Belgrade, University of Belgrade, Pregrevica 118, 11080 Belgrade, Serbia"}]}],"member":"1968","published-online":{"date-parts":[[2022,7,20]]},"reference":[{"key":"ref_1","unstructured":"Stiglitz, J.E. 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