Using Network Science to Evaluate and Enhance Hong Kong’s Bridging Role in One Belt One Road (OBOR)

Principal Investigator:

Prof Jonathan ZHU
Chair Professor, Department of Media and Communication;
Chair Professor, School of Data Science

Co-Principal Investigators:

• Prof Chong Wah NGO (Professor, Department of Computer Science)
• Dr Marko SKORIC (Associate Professor, Department of Media and Communication)
• Dr Shujun JIANG (Associate Professor, Department of Media and Creative Industries, United Arab Emirates University)

Project Period: 1 June 2020 – 31 May 2022

One Belt One Road (OBOR) is an ambitious and challenging initiative recently launched by mainland China to build new economic ties or strengthen existing ones with 60+ Eurasian nations. Capitalizing on the opportunities, Hong Kong SAR government has intended to play a super-connector or bridge role in the initiative. However, the idea of bridging role has not been clearly defined and empirically evaluated. The current project aims to use the emerging network science to supplement and complement existing knowledge and generate empirical evidence for policy on Hong Kong’s participation in OBOR.

Under the project, a prototype for an online data-driven monitoring system of OBOR network based on text, image, and video data from government websites, news channels, and social media platforms will be designed. The system will not only detect observable, formal, and strong ties among nations of OBOR, but also uncover hidden, informal, and weak ties among them. It is anticipated that the system will enable policy makers, business leaders, media professionals, and other stakeholders of OBOR to understand the existing strengths and weaknesses, as well as potential opportunities and threats for Hong Kong to play a bridging role.

The project aims to achieve the following research objectives:

1. To detect the global structure of the OBOR network, and to assess the impact of the network structure on Hong Kong’s bridging role.
2. To detect the local communities within the OBOR network, and to assess the impact of the community structures on Hong Kong’s bridging role.
3. To detect the influence of Hong Kong in the OBOR network, and to compare the position and influence between Hong Kong and potential competitors (e.g., Singapore, UAE, etc.).
4. To detect and understand the shaping forces of the above characteristics of OBOR network, local communities, and influential actors (e.g., mainland China, Hong Kong, etc.).
5. To forecast future directions of the OBOR network and understand their impact on Hong Kong’s bridging role.

 Publications

1. The Rumor Categorizer : An open-source software for analyzing rumor posts on Twitter
   Bodaghi, A., Oliveira, J. & Zhu, J. J. H., May 2023, In: Software Impacts. 12, 100232.
2. The spatial dissemination of COVID-19 and associated socio-economic consequences
   Zhang, Y., Wang, L., Zhu, J. J. H. & Wang, X., Feb 2023, In: Journal of the Royal Society Interface. 19, 187, 20210662.
3. The Olympic Gold Medalists on Instagram : A Data Mining Approach to Study User Characteristics
   Bodaghi, A. & Zhu, J. J. H., May 2022, In: Computer Networks, Big Data and IoT: Proceedings of ICCBI 2021. Pandian, A. P., Fernando, X. & Wang, H. (eds.). Singapore: Springer, p. 761-773 (Lecture Notes on Data Engineering and Communications Technologies; vol. 117).
4. Moreau Envelope Augmented Lagrangian Method for Nonconvex Optimization with Linear Constraints
   Zeng, J., Yin, W. and Zhou, D.X., Apr 2022, In: Journal of Scientific Computing, 91(2), p.61.
5. Predicting information exposure and continuous consumption : self-level interest similarity, peer-level interest similarity and global popularity
   Guan, L., Zhang, Y. & Zhu, J. J. H., Mar 2022, In: Online Information Review. 46, 2, p. 337-355
6. The fake news graph analyzer : An open-source software for characterizing spreaders in large diffusion graphs
   Bodaghi, A., Oliveira, J. & Zhu, J. J. H., Nov 2021, In: Software Impacts. 10, 100182.
7. Theory of deep convolutional neural networks III: Approximating radial functions
   Mao, T., Shi, Z. and Zhou, D.X., Oct 2021, In: Neural Networks, 144, pp.778-790.
8. On ADMM in Deep Learning: Convergence and Saturation-Avoidance
   Zeng, J., Lin, S.B., Yao, Y. and Zhou, D.X., Sep 2021, In: The Journal of Machine Learning Research, 22(1), pp.9024-9090.
9. Robust Kernel-based Distribution Regression
   Yu, Z., Ho, D.W., Shi, Z. and Zhou, D.X., Sep 2021, In: Inverse Problems, 37(10), p.105014.
10. Towards Understanding the Spectral Bias of Deep Learning
    Cao, Y., Fang, Z., Wu, Y., Zhou, D.X. and Gu, Q., Aug 2021, In: Proceedings of the Thirtieth International Joint Conference on Artificial Intelligence (IJCAI 2021). Zhou, Z. (ed.). International Joint Conferences on Artificial Intelligence, p. 2205-2211 (IJCAI International Joint Conference on Artificial Intelligence).
11. Impact of scientific, economic, geopolitical, and cultural factors on international research collaboration
    Hou, L., Pan, Y. & Zhu, J. J. H., Aug 2021, In: Journal of Informetrics. 15, 3, 101194.
12. Distributed regularized least squares with flexible Gaussian kernels
    Hu, T. and Zhou, D.X., Jul 2021, In: Applied and Computational Harmonic Analysis, 53, pp.349-377.
13. The Strength of Structural Diversity in Online Social Networks
    Zhang, Y., Wang, L., Zhu, J. J. H., Wang, X. & Pentland, A. S., May 2021, In: Research. 2021, 9831621.
14. Conspiracy vs science : A large-scale analysis of online discussion cascades
    Zhang, Y., Wang, L., Zhu, J. J. H. & Wang, X., Mar 2021, In: World Wide Web. 24, 2, p. 585–606
15. Jumping over the network threshold of information diffusion : testing the threshold hypothesis of social influence
    Wang, C. & Zhu, J. J. H, Feb 2021, In: Internet Research. 31, 5, p. 1677-1694
16. Depth Selection for Deep ReLU Nets in Feature Extraction and Generalization
    Han, Z., Yu, S., Lin, S.B. and Zhou, D.X., Oct 2020, In: IEEE Transactions on Pattern Analysis and Machine Intelligence, 44(4), pp.1853-1868.
17. Realization of Spatial Sparseness by Deep ReLU Nets With Massive Data
    Chui, C.K., Lin, S.B., Zhang, B. and Zhou, D.X., Oct 2020, In: IEEE Transactions on Neural Networks and Learning Systems, 33(1), pp.229-243.
18. From Filled to Empty Time Intervals : Quantifying Online Behaviors with Digital Traces
    Peng, T., Zhou, Y. & Zhu, J. J. H., Oct 2020, In: Communication Methods and Measures. 14, 4, p. 219–238
19. Distributed Kernel Ridge Regression with Communications
    Zhou, D.X., Apr 2020, In: The Journal of Machine Learning Research, 21(1), pp.3718-3755.