Identification and Ranking of High-Risk Nodes in Complex Financial Networks

Yishuo Wu; Zhongjun Li

doi:doi:10.11648/j.ijebo.20231103.12

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Identification and Ranking of High-Risk Nodes in Complex Financial Networks

Yishuo Wu, Zhongjun Li

Published in International Journal of Economic Behavior and Organization (Volume 11, Issue 3)

Received: 18 June 2023 Accepted: 12 July 2023 Published: 17 July 2023

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Abstract

Financial markets are ever closer interconnected, the superimposed impact and scope of multiple systemic financial risks such as epidemics, wars, and supply chains are expanding day by day, and the risk of contagion and spread of financial crises has become a problem that cannot be ignored. With the deepening of the theoretical research of complex networks, the combination of systemic financial risks and complex networks has become more closely connected. Financial networks are characterized by the accumulation of multiple risks, and their overall stability depends on the stability of specific nodes in the network. Therefore, accurately identifying and ranking high-risk nodes has become a difficult point restricting the improvement of resource utilization efficiency, and it has become extremely critical. Based on the complex network theory, firstly, a directed graph from the massive financial risk warning information is constructed in this paper, and obtains the subject community of financial risk incidents. Then the superimposed impact of multiple systemic financial risks is measured from the three dimensions of complex network topology, financial risk behavior and risk propagation probability. Finally, the influence distribution and dissemination rules of nodes are analyzed in the subject community, and a high-risk nodes identification algorithm CIRA (community-based identifying and ranking algorithm) is proposed. Experiments show that the algorithm can effectively mine potential high-risk nodes and obtain higher risk density.

Published in	International Journal of Economic Behavior and Organization (Volume 11, Issue 3)
DOI	10.11648/j.ijebo.20231103.12
Page(s)	125-134
Creative Commons	This is an Open Access article, distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution and reproduction in any medium or format, provided the original work is properly cited.
Copyright	Copyright © The Author(s), 2024. Published by Science Publishing Group

Keywords

Systemic Financial Risk, Complex Network, Network Topology Structure, Risk Behavior, Risk Spreading

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APA Style

Yishuo Wu, Zhongjun Li. (2023). Identification and Ranking of High-Risk Nodes in Complex Financial Networks. International Journal of Economic Behavior and Organization, 11(3), 125-134. https://doi.org/10.11648/j.ijebo.20231103.12

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ACS Style

Yishuo Wu; Zhongjun Li. Identification and Ranking of High-Risk Nodes in Complex Financial Networks. Int. J. Econ. Behav. Organ. 2023, 11(3), 125-134. doi: 10.11648/j.ijebo.20231103.12

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AMA Style

Yishuo Wu, Zhongjun Li. Identification and Ranking of High-Risk Nodes in Complex Financial Networks. Int J Econ Behav Organ. 2023;11(3):125-134. doi: 10.11648/j.ijebo.20231103.12

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@article{10.11648/j.ijebo.20231103.12,
  author = {Yishuo Wu and Zhongjun Li},
  title = {Identification and Ranking of High-Risk Nodes in Complex Financial Networks},
  journal = {International Journal of Economic Behavior and Organization},
  volume = {11},
  number = {3},
  pages = {125-134},
  doi = {10.11648/j.ijebo.20231103.12},
  url = {https://doi.org/10.11648/j.ijebo.20231103.12},
  eprint = {https://article.sciencepublishinggroup.com/pdf/10.11648.j.ijebo.20231103.12},
  abstract = {Financial markets are ever closer interconnected, the superimposed impact and scope of multiple systemic financial risks such as epidemics, wars, and supply chains are expanding day by day, and the risk of contagion and spread of financial crises has become a problem that cannot be ignored. With the deepening of the theoretical research of complex networks, the combination of systemic financial risks and complex networks has become more closely connected. Financial networks are characterized by the accumulation of multiple risks, and their overall stability depends on the stability of specific nodes in the network. Therefore, accurately identifying and ranking high-risk nodes has become a difficult point restricting the improvement of resource utilization efficiency, and it has become extremely critical. Based on the complex network theory, firstly, a directed graph from the massive financial risk warning information is constructed in this paper, and obtains the subject community of financial risk incidents. Then the superimposed impact of multiple systemic financial risks is measured from the three dimensions of complex network topology, financial risk behavior and risk propagation probability. Finally, the influence distribution and dissemination rules of nodes are analyzed in the subject community, and a high-risk nodes identification algorithm CIRA (community-based identifying and ranking algorithm) is proposed. Experiments show that the algorithm can effectively mine potential high-risk nodes and obtain higher risk density.},
 year = {2023}
}

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TY  - JOUR
T1  - Identification and Ranking of High-Risk Nodes in Complex Financial Networks
AU  - Yishuo Wu
AU  - Zhongjun Li
Y1  - 2023/07/17
PY  - 2023
N1  - https://doi.org/10.11648/j.ijebo.20231103.12
DO  - 10.11648/j.ijebo.20231103.12
T2  - International Journal of Economic Behavior and Organization
JF  - International Journal of Economic Behavior and Organization
JO  - International Journal of Economic Behavior and Organization
SP  - 125
EP  - 134
PB  - Science Publishing Group
SN  - 2328-7616
UR  - https://doi.org/10.11648/j.ijebo.20231103.12
AB  - Financial markets are ever closer interconnected, the superimposed impact and scope of multiple systemic financial risks such as epidemics, wars, and supply chains are expanding day by day, and the risk of contagion and spread of financial crises has become a problem that cannot be ignored. With the deepening of the theoretical research of complex networks, the combination of systemic financial risks and complex networks has become more closely connected. Financial networks are characterized by the accumulation of multiple risks, and their overall stability depends on the stability of specific nodes in the network. Therefore, accurately identifying and ranking high-risk nodes has become a difficult point restricting the improvement of resource utilization efficiency, and it has become extremely critical. Based on the complex network theory, firstly, a directed graph from the massive financial risk warning information is constructed in this paper, and obtains the subject community of financial risk incidents. Then the superimposed impact of multiple systemic financial risks is measured from the three dimensions of complex network topology, financial risk behavior and risk propagation probability. Finally, the influence distribution and dissemination rules of nodes are analyzed in the subject community, and a high-risk nodes identification algorithm CIRA (community-based identifying and ranking algorithm) is proposed. Experiments show that the algorithm can effectively mine potential high-risk nodes and obtain higher risk density.
VL  - 11
IS  - 3
ER  -

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Author Information

Yishuo Wu

International Department Sino-American Class, Jinling High School, Nanjing, China
Zhongjun Li

School of Management Science and Engineering, Southwestern University of Finance and Economics, Chengdu, China

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Plain Text BibTeX RIS

APA Style

Yishuo Wu, Zhongjun Li. (2023). Identification and Ranking of High-Risk Nodes in Complex Financial Networks. International Journal of Economic Behavior and Organization, 11(3), 125-134. https://doi.org/10.11648/j.ijebo.20231103.12

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ACS Style

Yishuo Wu; Zhongjun Li. Identification and Ranking of High-Risk Nodes in Complex Financial Networks. Int. J. Econ. Behav. Organ. 2023, 11(3), 125-134. doi: 10.11648/j.ijebo.20231103.12

Copy | Download

AMA Style

Yishuo Wu, Zhongjun Li. Identification and Ranking of High-Risk Nodes in Complex Financial Networks. Int J Econ Behav Organ. 2023;11(3):125-134. doi: 10.11648/j.ijebo.20231103.12

Copy | Download

@article{10.11648/j.ijebo.20231103.12,
  author = {Yishuo Wu and Zhongjun Li},
  title = {Identification and Ranking of High-Risk Nodes in Complex Financial Networks},
  journal = {International Journal of Economic Behavior and Organization},
  volume = {11},
  number = {3},
  pages = {125-134},
  doi = {10.11648/j.ijebo.20231103.12},
  url = {https://doi.org/10.11648/j.ijebo.20231103.12},
  eprint = {https://article.sciencepublishinggroup.com/pdf/10.11648.j.ijebo.20231103.12},
  abstract = {Financial markets are ever closer interconnected, the superimposed impact and scope of multiple systemic financial risks such as epidemics, wars, and supply chains are expanding day by day, and the risk of contagion and spread of financial crises has become a problem that cannot be ignored. With the deepening of the theoretical research of complex networks, the combination of systemic financial risks and complex networks has become more closely connected. Financial networks are characterized by the accumulation of multiple risks, and their overall stability depends on the stability of specific nodes in the network. Therefore, accurately identifying and ranking high-risk nodes has become a difficult point restricting the improvement of resource utilization efficiency, and it has become extremely critical. Based on the complex network theory, firstly, a directed graph from the massive financial risk warning information is constructed in this paper, and obtains the subject community of financial risk incidents. Then the superimposed impact of multiple systemic financial risks is measured from the three dimensions of complex network topology, financial risk behavior and risk propagation probability. Finally, the influence distribution and dissemination rules of nodes are analyzed in the subject community, and a high-risk nodes identification algorithm CIRA (community-based identifying and ranking algorithm) is proposed. Experiments show that the algorithm can effectively mine potential high-risk nodes and obtain higher risk density.},
 year = {2023}
}

Copy | Download

TY  - JOUR
T1  - Identification and Ranking of High-Risk Nodes in Complex Financial Networks
AU  - Yishuo Wu
AU  - Zhongjun Li
Y1  - 2023/07/17
PY  - 2023
N1  - https://doi.org/10.11648/j.ijebo.20231103.12
DO  - 10.11648/j.ijebo.20231103.12
T2  - International Journal of Economic Behavior and Organization
JF  - International Journal of Economic Behavior and Organization
JO  - International Journal of Economic Behavior and Organization
SP  - 125
EP  - 134
PB  - Science Publishing Group
SN  - 2328-7616
UR  - https://doi.org/10.11648/j.ijebo.20231103.12
AB  - Financial markets are ever closer interconnected, the superimposed impact and scope of multiple systemic financial risks such as epidemics, wars, and supply chains are expanding day by day, and the risk of contagion and spread of financial crises has become a problem that cannot be ignored. With the deepening of the theoretical research of complex networks, the combination of systemic financial risks and complex networks has become more closely connected. Financial networks are characterized by the accumulation of multiple risks, and their overall stability depends on the stability of specific nodes in the network. Therefore, accurately identifying and ranking high-risk nodes has become a difficult point restricting the improvement of resource utilization efficiency, and it has become extremely critical. Based on the complex network theory, firstly, a directed graph from the massive financial risk warning information is constructed in this paper, and obtains the subject community of financial risk incidents. Then the superimposed impact of multiple systemic financial risks is measured from the three dimensions of complex network topology, financial risk behavior and risk propagation probability. Finally, the influence distribution and dissemination rules of nodes are analyzed in the subject community, and a high-risk nodes identification algorithm CIRA (community-based identifying and ranking algorithm) is proposed. Experiments show that the algorithm can effectively mine potential high-risk nodes and obtain higher risk density.
VL  - 11
IS  - 3
ER  -

Copy | Download