Skip to main content
SpringerLink
Log in

Analysis of Corporate Community of Interest Relationships in Combination with Multiple Network

  • Conference paper
  • First Online:
Advances in Computer Graphics (CGI 2023)

Part of the book series: Lecture Notes in Computer Science ((LNCS,volume 14497))

Included in the following conference series:

  • 175 Accesses

Abstract

Visualizing the complex relationship among enterprises is ponderable to help enterprises and institutions to find potential risks. Small and medium-size enterprises’ (SMEs) loans have higher risk and non-performing rates than other types of enterprises, which are prone to form the complex relationship. Nowadays, the analysis of enterprises’ relationships networks mainly focus on the guaranteed relationships among enterprises, but it lacks the holistic analysis of the enterprise community of interest. To address these issues, the concepts of the enterprise community of interest and the investment model withing enterprise community of interest are proposed; The centrality, density, and network diameter algorithms in graph theory are used to evaluate the network of enterprise community of interest; The problem of graph isomorphism are used to query the network of users interested enterprises’ relationships; The portrait of enterprise is used to evaluate the enterprise community of interest; In the end, we study the impact of debt relationship among enterprise community of interest. Based on these ideas, to verify the effectiveness of the method, an enterprise relationship network analysis system which included 6745 enterprise nodes and 7435 enterprise relationship data of Shanghai is developed.

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Log in via an institution
Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 59.99
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 79.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

References

  1. Stroebel, J.: Asymmetric information about collateral values. J. Financ. 71(3), 1071–1112 (2016)

    Article  MathSciNet  Google Scholar 

  2. Silva, J.S., Saraiva, A.M.: A methodology for applying social network analysis metrics to biological interaction networks. In: Proceedings of the 2015 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining, pp. 1300–1307 (2015)

    Google Scholar 

  3. Niu, Z., Li, R., Wu, J., et al.: iconviz: Interactive visual exploration of the default contagion risk of net-worked-guarantee loans. In: 2020 IEEE Conference on Visual Analytics Science and Technology (VAST). IEEE, pp. 84–94 (2020). Author, F.: Contribution title. In: 9th International Proceedings on Proceedings, pp. 1–2. Publisher, Location (2010)

    Google Scholar 

  4. Deyoung, R., Gron, A., Torna, G., et al.: Risk overhang and loan portfolio decisions: small business loan supply before and during the financial crisis. J. Financ. 70(6), 2451–2488 (2015)

    Article  Google Scholar 

  5. Garvin, W.J.: The small business capital gap: the special case of minority enterprise. J. Financ. 26(2), 445–457 (1971)

    Article  Google Scholar 

  6. Ng, C.K., Smith, J.K., Smith, R.L.: Evidence on the determinants of credit terms used in interfirm trade. J. Financ. 54(3), 1109–1129 (1999)

    Article  Google Scholar 

  7. Niu, Z., Cheng, D., Zhang, L., et al.: Visual analytics for networked-guarantee loans risk management. In: 2018 IEEE Pacific Visualization Symposium (PacificVis). IEEE, pp. 160–169 (2018)

    Google Scholar 

  8. Sarlin, P., Marghescu, D.: Visual predictions of currency crises using self-organizing maps. Intell. Syst. Account. Financ. Manag. 18(1), 15–38 (2011)

    Article  Google Scholar 

  9. Shen, F., Zhao, X., Kou, G., et al.: A new deep learning ensemble credit risk evaluation model with an improved synthetic minority oversampling technique. Appl. Soft Comput. 98, 106852 (2021)

    Article  Google Scholar 

  10. Friewald, N., Wagner, C., Zechner, J.: The cross‐section of credit risk premia and equity returns. J. Financ. 69(6), 2419–2469 (2014)

    Article  Google Scholar 

  11. Sun, H., Guo, M.: Credit risk assessment model of small and medium-sized enterprise based on logistic regression. In: 2015 IEEE International Conference on Industrial Engineering and Engineering Management (IEEM). IEEE (2015)

    Google Scholar 

  12. Cheng, D., Niu, Z., Zhang, L.: Risk research of large-scale unbalanced guarantee network loan. Chin. J. Comput. 43(04), 668–682 (2020)

    Google Scholar 

  13. Bian, W.-L.: Research on credit risk management of commercial banks in China. New Econ. 2(2), 50–51 (2016)

    Google Scholar 

  14. Nijskens, R., Wagner, W.: Credit risk transfer activities and systemic risk: how banks became less risky individually but posed greater risks to the financial system at the same time. J. Bank Fiannc. 35(6), 1391–1398 (2011)

    Article  Google Scholar 

  15. Flood, M.D., Lemieux, V.L., Varga, M., Wong, B.W.: The application of visual analytics to financial stability monitoring. J. Financ. Stab. 27, 180–197 (2016)

    Article  Google Scholar 

  16. Sarlin, P.: Macroprudential oversight, risk communication and visualization. J. Financ. Stab. 27, 160–179 (2016)

    Article  Google Scholar 

  17. Cheng, D., Niu, Z., Liu, X., Zhang, L.: Risk assessment of contagion paths in complex guaran-tee networks. Sci. China: Inform. Sci. 51(07), 1068–1083 (2021)

    Google Scholar 

  18. Chen, W., Guo, F., Han, D., et al.: Structure-based suggestive exploration: a new approach for effective exploration of large networks. IEEE Trans. Visual Comput. Graphics 25(1), 555–565 (2018)

    Article  Google Scholar 

  19. Zhang, J., Song, Z.: Research on knowledge graph for quantification of relationship be-tween enterprises and recognition. In: 2019 4th IEEE International Conference on Cybernetics (Cybconf). IEEE (2019)

    Google Scholar 

  20. Rönnqvist, S., Sarlin, P.: Bank networks from text: interrelations, centrality and determinants. Quant. Financ. 15(10), 1619–1635 (2015)

    Article  MathSciNet  Google Scholar 

  21. Didimo, W., Liotta, G., Montecchiani, F., Palladino, P.: An advanced network visualization system for financial crime detection. In: Visualization Symposium (PacificVis), 2011 IEEE Pacific, pp. 203–210. IEEE (2011)

    Google Scholar 

  22. Suh, A., Hajij, M., Wang, B., et al.: Persistent homology guided force-directed graph layouts. IEEE Trans. Visual Comput. Graphics 26(1), 697–707 (2019)

    Google Scholar 

  23. Goswami, S.: Enhancement of the effective bandwidth available to a client host in a network by using multiple network devices concurrently. In: International Conference on Information and Communication Technology for Competitive Strategies. Springer Nature, Singapore (2022). https://doi.org/10.1007/978-981-19-9638-2_13

  24. Yadav, S., Raj, R.: Power efficient network selector placement in control plane of multiple networks-on-chip. J. Supercomput. 78(5), 6664–6695 (2022)

    Article  Google Scholar 

  25. Bharatula, S., Murthy, B.S.: A novel spectrum sensing technique for multiple network scenario. In: Proceedings of International Conference on Communication and Artificial Intelligence: ICCAI 2021. Springer Nature, Singapore (2022). https://doi.org/10.1007/978-981-19-0976-4_9

  26. Xing, G., et al.: Improving UWB ranging accuracy via multiple network model with second order motion prediction. Cluster Comput. 1–12 (2023)

    Google Scholar 

Download references

Acknowledgement

This work was supported by Natural Science Foundation of Sichuan Province (Grant No. 2022NSFSC0961) the Ph.D. Research Foundation of Southwest University of Science and Technology (Grant No. 19zx7144) the Special Research Foundation of China (Mianyang) Science and Technology City Network Emergency Management Research Center (Grant No. WLYJGL2023ZD04).

Author information

Authors and Affiliations

  1. School of Computer Science and Technology, Southwest University of Science and Technology, Mianyang, 621010, Sichuan, China

    Yipan Liu, Song Wang, Hao Hu & Shijie Chen

Authors
  1. Yipan Liu
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Song Wang
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Hao Hu
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Shijie Chen
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Song Wang .

Editor information

Editors and Affiliations

  1. Shanghai Jiao Tong University, Shanghai, China

    Bin Sheng

  2. Shanghai Jiao Tong University, Shanghai, China

    Lei Bi

  3. University of Sydney, Sydney, NSW, Australia

    Jinman Kim

  4. MIRALab-CUI, University of Geneva, Carouge, Geneve, Switzerland

    Nadia Magnenat-Thalmann

  5. Swiss Federal Institute of Technology, Lausanne, Switzerland

    Daniel Thalmann

Rights and permissions

Reprints and permissions

Copyright information

© 2024 The Author(s), under exclusive license to Springer Nature Switzerland AG

About this paper

Check for updates. Verify currency and authenticity via CrossMark

Cite this paper

Liu, Y., Wang, S., Hu, H., Chen, S. (2024). Analysis of Corporate Community of Interest Relationships in Combination with Multiple Network. In: Sheng, B., Bi, L., Kim, J., Magnenat-Thalmann, N., Thalmann, D. (eds) Advances in Computer Graphics. CGI 2023. Lecture Notes in Computer Science, vol 14497. Springer, Cham. https://doi.org/10.1007/978-3-031-50075-6_8

Download citation

  • DOI: https://doi.org/10.1007/978-3-031-50075-6_8

  • Published:

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-031-50074-9

  • Online ISBN: 978-3-031-50075-6

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics

Search

Navigation