IDEAS home Printed from https://ideas.repec.org/a/pal/palcom/v11y2024i1d10.1057_s41599-024-02823-x.html
   My bibliography  Save this article

Identifying risks in temporal supernetworks: an IO-SuperPageRank algorithm

Author

Listed:
  • Yijun Liu

    (Chinese Academy of Sciences
    University of Chinese Academy of Sciences)

  • Xiaokun Jin

    (Chinese Academy of Sciences
    University of Chinese Academy of Sciences)

  • Yunrui Zhang

    (Chinese Academy of Sciences
    University of Chinese Academy of Sciences)

Abstract

Complex systems pose risks characterized by factors such as uncertainty, nonlinearity, and diversity, making traditional risk measurement methods based on a probabilistic framework inadequate. Supernetworks can effectively model complex systems, and temporal supernetworks can capture the dynamic evolution of these systems. From the perspective of network stability, supernetworks can aid in risk identification for complex systems. In this paper, an IO-SuperPageRank algorithm is proposed based on the supernetwork topological structure. This algorithm reveals network instability by calculating changes in node importance, thereby helping to identify risks in complex systems. To validate the effectiveness of this algorithm, a four-layer supernetwork composed of scale-free networks is constructed. Simulated experiments are conducted to assess the impact of changes in intralayer edge numbers, intralayer node numbers, and interlayer superedge numbers on the risk indicator IO value. Linear regression and multiple tests were used to validate these relationships. The experiments show that changes in the three network topological indicators all bring about risks, with changes in intralayer node numbers having the most significant correlation with the risk indicator IO value. Compared to traditional measures of network node centrality and connectivity, this algorithm can more accurately predict the impact of node updates on network stability. Additionally, this paper collected trade data for crude oil, chemical light oil, man-made filaments and man-made staple fibers from the UN Comtrade Database. We constructed a man-made filaments and fibers supply chain temporal supernetwork, utilizing the algorithm to identify supply chain risks from December 2020 to October 2023. The study revealed that the algorithm effectively identified risks brought about by changes in international situations such as the Russia-Ukraine war, Israel–Hamas conflict, and the COVID-19 pandemic. This demonstrated the algorithm’s effectiveness in empirical analysis. In the future, we plan to further expand its application based on different scenarios, assess risks by analyzing changes in specific system elements, and implement effective risk intervention measures.

Suggested Citation

  • Yijun Liu & Xiaokun Jin & Yunrui Zhang, 2024. "Identifying risks in temporal supernetworks: an IO-SuperPageRank algorithm," Palgrave Communications, Palgrave Macmillan, vol. 11(1), pages 1-21, December.
  • Handle: RePEc:pal:palcom:v:11:y:2024:i:1:d:10.1057_s41599-024-02823-x
    DOI: 10.1057/s41599-024-02823-x
    as

    Download full text from publisher

    File URL: http://link.springer.com/10.1057/s41599-024-02823-x
    File Function: Abstract
    Download Restriction: Access to full text is restricted to subscribers.

    File URL: https://libkey.io/10.1057/s41599-024-02823-x?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    As the access to this document is restricted, you may want to

    for a different version of it.

    References listed on IDEAS

    as
    1. Sybil Derrible, 2017. "Complexity in future cities: the rise of networked infrastructure," International Journal of Urban Sciences, Taylor & Francis Journals, vol. 21(0), pages 68-86, August.
    2. Rickard Nyman & Sujit Kapadia & David Tuckett & David Gregory & Paul Ormerod & Robert Smith, 2018. "News and narratives in financial systems: exploiting big data for systemic risk assessment," Bank of England working papers 704, Bank of England.
    3. Tian, Ru-Ya & Zhang, Xue-Fu & Liu, Yi-Jun, 2015. "SSIC model: A multi-layer model for intervention of online rumors spreading," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 427(C), pages 181-191.
    4. Zhuo-Ming Ren & An Zeng & Yi-Cheng Zhang, 2020. "Bridging nestedness and economic complexity in multilayer world trade networks," Palgrave Communications, Palgrave Macmillan, vol. 7(1), pages 1-8, December.
    5. Christian Scheve & Markus Lange, 2023. "Risk entanglement and the social relationality of risk," Palgrave Communications, Palgrave Macmillan, vol. 10(1), pages 1-10, December.
    6. Crispim, José & Fernandes, Jorge & Rego, Nazaré, 2020. "Customized risk assessment in military shipbuilding," Reliability Engineering and System Safety, Elsevier, vol. 197(C).
    7. Unai Alvarez-Rodriguez & Federico Battiston & Guilherme Ferraz Arruda & Yamir Moreno & Matjaž Perc & Vito Latora, 2021. "Evolutionary dynamics of higher-order interactions in social networks," Nature Human Behaviour, Nature, vol. 5(5), pages 586-595, May.
    8. Ritesh Ojha & Abhijeet Ghadge & Manoj Kumar Tiwari & Umit S. Bititci, 2018. "Bayesian network modelling for supply chain risk propagation," International Journal of Production Research, Taylor & Francis Journals, vol. 56(17), pages 5795-5819, September.
    9. Xie, Xiaofeng & Shi, Xinyu & Gu, Jing & Xu, Xun, 2023. "Examining the Contagion Effect of Credit Risk in a Supply Chain under Trade Credit and Bank Loan Offering," Omega, Elsevier, vol. 115(C).
    10. Aven, Terje, 2016. "Risk assessment and risk management: Review of recent advances on their foundation," European Journal of Operational Research, Elsevier, vol. 253(1), pages 1-13.
    11. Kraude, Richard & Narayanan, Sriram & Talluri, Srinivas, 2022. "Evaluating the performance of supply chain risk mitigation strategies using network data envelopment analysis," European Journal of Operational Research, Elsevier, vol. 303(3), pages 1168-1182.
    12. Wen, Shigang & Li, Jianping & Huang, Chuangxia & Zhu, Xiaoqian, 2023. "Extreme risk spillovers among traditional financial and FinTech institutions: A complex network perspective," The Quarterly Review of Economics and Finance, Elsevier, vol. 88(C), pages 190-202.
    13. Marzieh Mehrjoo & Zbigniew J. Pasek, 2016. "Risk assessment for the supply chain of fast fashion apparel industry: a system dynamics framework," International Journal of Production Research, Taylor & Francis Journals, vol. 54(1), pages 28-48, January.
    14. Yuexu Zhao & Weiqi Xu, 2023. "Measurement of risk spillover effect based on EV-Copula method," Palgrave Communications, Palgrave Macmillan, vol. 10(1), pages 1-10, December.
    15. Nyman, Rickard & Kapadia, Sujit & Tuckett, David, 2021. "News and narratives in financial systems: Exploiting big data for systemic risk assessment," Journal of Economic Dynamics and Control, Elsevier, vol. 127(C).
    16. Michael M. Danziger & Albert-László Barabási, 2022. "Recovery coupling in multilayer networks," Nature Communications, Nature, vol. 13(1), pages 1-8, December.
    17. Estrada, Ernesto & Rodríguez-Velázquez, Juan A., 2006. "Subgraph centrality and clustering in complex hyper-networks," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 364(C), pages 581-594.
    18. Xu, Runjie & Mi, Chuanmin & Mierzwiak, Rafał & Meng, Runyu, 2020. "Complex network construction of Internet finance risk," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 540(C).
    19. Linqing Liu & Weiran Wang & Xiaofei Yan & Mengyun Shen & Haizhi Chen, 2023. "The cascade influence of grain trade shocks on countries in the context of the Russia-Ukraine conflict," Palgrave Communications, Palgrave Macmillan, vol. 10(1), pages 1-28, December.
    20. Andrew Fielder & Sandra König & Emmanouil Panaousis & Stefan Schauer & Stefan Rass, 2018. "Risk Assessment Uncertainties in Cybersecurity Investments," Games, MDPI, vol. 9(2), pages 1-14, June.
    21. Choi, Tsan-Ming & Wen, Xin & Sun, Xuting & Chung, Sai-Ho, 2019. "The mean-variance approach for global supply chain risk analysis with air logistics in the blockchain technology era," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 127(C), pages 178-191.
    22. Thi Huong Tran & Mario Dobrovnik & Sebastian Kummer, 2018. "Supply chain risk assessment: a content analysis-based literature review," International Journal of Logistics Systems and Management, Inderscience Enterprises Ltd, vol. 31(4), pages 562-591.
    Full references (including those not matched with items on IDEAS)

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Nishat Alam Choudhary & Shalabh Singh & Tobias Schoenherr & M. Ramkumar, 2023. "Risk assessment in supply chains: a state-of-the-art review of methodologies and their applications," Annals of Operations Research, Springer, vol. 322(2), pages 565-607, March.
    2. Fraccaroli, Nicolò & Giovannini, Alessandro & Jamet, Jean-François & Persson, Eric, 2022. "Ideology and monetary policy. The role of political parties’ stances in the European Central Bank’s parliamentary hearings," European Journal of Political Economy, Elsevier, vol. 74(C).
    3. Borgioli, Stefano & Gallo, Giampiero M. & Ongari, Chiara, 2024. "Financial returns, sentiment and market volatility. A dynamic assessment," Working Paper Series 2999, European Central Bank.
    4. Yuting Chen & Don Bredin & Valerio Potì & Roman Matkovskyy, 2022. "COVID risk narratives: a computational linguistic approach to the econometric identification of narrative risk during a pandemic," Digital Finance, Springer, vol. 4(1), pages 17-61, March.
    5. Youngjoon Lee & Soohyon Kim & Ki Young Park, 2018. "Deciphering Monetary Policy Committee Minutes with Text Mining Approach: A Case of South Korea," Working papers 2018rwp-132, Yonsei University, Yonsei Economics Research Institute.
    6. Matteo Accornero & Mirko Moscatelli, 2018. "Listening to the buzz: social media sentiment and retail depositors' trust," Temi di discussione (Economic working papers) 1165, Bank of Italy, Economic Research and International Relations Area.
    7. Kohns, David & Bhattacharjee, Arnab, 2023. "Nowcasting growth using Google Trends data: A Bayesian Structural Time Series model," International Journal of Forecasting, Elsevier, vol. 39(3), pages 1384-1412.
    8. Aren Selim & Hamamci Hatice Nayman, 2023. "Mediating Effect of Pleasure-Seeking and Loss Aversion in the Relationship Between Phantasy and Financial Risk Tolerance and the Moderating Role of Confidence," Folia Oeconomica Stetinensia, Sciendo, vol. 23(2), pages 24-44, December.
    9. Pablo Pastory y Camarasa & Martien Lamers, 2023. "Do Actions Follow Words? How bank sentiment predicts credit growth," Working Papers of Faculty of Economics and Business Administration, Ghent University, Belgium 23/1073, Ghent University, Faculty of Economics and Business Administration.
    10. Kurowski, Łukasz & Smaga, Paweł, 2023. "Analysing financial stability reports as crisis predictors with the use of text-mining," The Journal of Economic Asymmetries, Elsevier, vol. 28(C).
    11. Young Joon Lee & Soohyon Kim & Ki Young Park, 2019. "Deciphering Monetary Policy Board Minutes with Text Mining: The Case of South Korea," Korean Economic Review, Korean Economic Association, vol. 35, pages 471-511.
    12. Ashwin,Julian & Rao,Vijayendra & Biradavolu,Monica Rao & Chhabra,Aditya & Haque,Arshia & Khan,Afsana Iffat & Krishnan,Nandini, 2022. "A Method to Scale-Up Interpretative Qualitative Analysis, with an Application toAspirations in Cox’s Bazaar, Bangladesh," Policy Research Working Paper Series 10046, The World Bank.
    13. Fernandez, Raul & Palma Guizar, Brenda & Rho, Caterina, 2021. "A sentiment-based risk indicator for the Mexican financial sector," Latin American Journal of Central Banking (previously Monetaria), Elsevier, vol. 2(3).
    14. Ching Hsu & Tina Yu & Shu-Heng Chen, 2021. "Narrative economics using textual analysis of newspaper data: new insights into the U.S. Silver Purchase Act and Chinese price level in 1928–1936," Journal of Computational Social Science, Springer, vol. 4(2), pages 761-785, November.
    15. Paula T. Wang & Musa Malik & René Weber, 2025. "Highlighting the Role of Morality in News Framing and Its Short-Term Effects on Stock Market Fluctuations," IJFS, MDPI, vol. 13(2), pages 1-19, June.
    16. Alistair Macaulay & Wenting Song, 2022. "Narrative-Driven Fluctuations in Sentiment: Evidence Linking Traditional and Social Media," Economics Series Working Papers 973, University of Oxford, Department of Economics.
    17. Amer Demirovic & Ali Kabiri & David Tuckett & Rickard Nyman, 2020. "A common risk factor and the correlation between equity and corporate bond returns," Journal of Asset Management, Palgrave Macmillan, vol. 21(2), pages 119-134, March.
    18. Mark Fenton‐O'Creevy & David Tuckett, 2022. "Selecting futures: The role of conviction, narratives, ambivalence, and constructive doubt," Futures & Foresight Science, John Wiley & Sons, vol. 4(3-4), September.
    19. Svetlana Drobyazko & Anna Barwinska-Malajowicz & Boguslaw Slusarczyk & Olga Chubukova & Taliat Bielialov, 2020. "Risk Management in the System of Financial Stability of the Service Enterprise," JRFM, MDPI, vol. 13(12), pages 1-15, November.
    20. Mariña Martínez-Malvar & Laura Baselga-Pascual, 2020. "Bank Risk Determinants in Latin America," Risks, MDPI, vol. 8(3), pages 1-20, September.

    More about this item

    Statistics

    Access and download statistics

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:pal:palcom:v:11:y:2024:i:1:d:10.1057_s41599-024-02823-x. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Sonal Shukla or Springer Nature Abstracting and Indexing (email available below). General contact details of provider: https://www.nature.com/ .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.