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职业迁徙
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Research Themes
Data collected from generic complex systems can be represented in the form of networks, ranging from online social networks, physical contact networks to critical infrastructures. My team focuses on Network Data Science and aims to develop methodologies to predict, model and control
processes (such as information, disease, failure propagation and social or financial contagion) on networks combining network and data science approaches.
The prediction problems addressed include the prediction of late payment of invoices, performance of companies in the network of monetary transactions among companies and the prediction of outbreak size of information/epidemic spreading.
Modeling and Control: We have developed methodologies to understand how the underlying interdependent and time-evolving network influences a dynamic process on the network. Such understanding enables the optimization of network topology to be robust against virus and failure propagation or efficient in information diffusion. Furthermore, strategies to mitigate the spread of epidemics/information via e.g. blocking network connections and have been developed. Currently, we are addressing the additional complexity, the higher-order (group interaction) and time-evolving nature of networks, especially the problem of predicting and modelling of such networks.
Our ambition is to discover the underlying mechanism or process of a complex social-physical ystem that we don’t understand. Such interpretation of data to the extent that we could further optimize the system is deemed as the fore-runner of AI-Networking. This ambition is being pursued via our current projects like NOW-TOP and KPN-TUDelft AI Networking and NWO-KIC Fort-Port project. We focus on application domains such as financial, social, urban systems, criminal organization and critical infrastructures (telecommunications and traffic networks).
Data collected from generic complex systems can be represented in the form of networks, ranging from online social networks, physical contact networks to critical infrastructures. My team focuses on Network Data Science and aims to develop methodologies to predict, model and control
processes (such as information, disease, failure propagation and social or financial contagion) on networks combining network and data science approaches.
The prediction problems addressed include the prediction of late payment of invoices, performance of companies in the network of monetary transactions among companies and the prediction of outbreak size of information/epidemic spreading.
Modeling and Control: We have developed methodologies to understand how the underlying interdependent and time-evolving network influences a dynamic process on the network. Such understanding enables the optimization of network topology to be robust against virus and failure propagation or efficient in information diffusion. Furthermore, strategies to mitigate the spread of epidemics/information via e.g. blocking network connections and have been developed. Currently, we are addressing the additional complexity, the higher-order (group interaction) and time-evolving nature of networks, especially the problem of predicting and modelling of such networks.
Our ambition is to discover the underlying mechanism or process of a complex social-physical ystem that we don’t understand. Such interpretation of data to the extent that we could further optimize the system is deemed as the fore-runner of AI-Networking. This ambition is being pursued via our current projects like NOW-TOP and KPN-TUDelft AI Networking and NWO-KIC Fort-Port project. We focus on application domains such as financial, social, urban systems, criminal organization and critical infrastructures (telecommunications and traffic networks).
研究兴趣
论文共 107 篇作者统计合作学者相似作者
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Scientific Reportsno. 1 (2024): 1-13
INTERNATIONAL JOURNAL OF URBAN SCIENCESpp.1-22, (2023)
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arxiv(2023)
Appl. Netw. Sci.no. 1 (2023): 1-25
COMPLEX NETWORKS AND THEIR APPLICATIONS XI, COMPLEX NETWORKS 2022, VOL 2 (2023): 245-257
Louise Leibbrandt, Shilun Zhang,Marijn Roelvink, Stan Bergkamp, Xinqi Li,Lieselot Bisschop, Karin van Wingerde,Huijuan Wang
COMPLEX NETWORKS AND THEIR APPLICATIONS XI, COMPLEX NETWORKS 2022, VOL 2 (2023): 675-686
Complex Networks and Their Applications XI (2023): 661-673
International Workshop on Complex Networks & Their Applicationspp.461-472, (2023)
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