Jeroen Allard van Lidth de Jeude

  +39 3455795516

Jeroen Allard van Lidth de Jeude photo

Emergent structures in economic and financial networks

Network analysis, data science, econophysics

I work on network analysis and data drive modellilng of economic and financial systems. 

On the identification of significant structures in financial networks I work with the Dutch central bank (De Nederlandse Bank) and the central bank of Mexico (Banco de México).

With data on ownership, directors, patents and stock informaion we are mapping the interactions of the many relations between companies, to understand the ecosystem of corporate networks. (With Prof. Tomaso Aste at UCL)




I joined IMT in November 2015 as a PhD student in the Economics, Management and Data Science track. I am interested in complexity science and its applications, data science, econophysics, modelling of real world systems and risk analysis. I am part of the NETWORKS research unit from Prof. Guido Caldarelli.
I collaborate with the Dutch central bank (De Nederlandse Bank) and the Central bank of Mexico (Banco de México) and the Financial computing and analytics group at UCL London.
My main research topic is on the mesoscale structures of financial and economic networks; core-periphery and bow-tie structure detection, block structure reconstruction, link prediction, and motif analysis. Analyzing the multilayer Dutch interbank networks and European sector exposure network and corporate networks (ownership, board interlocks, R&D). I also work on inventor dynamics during firm mergers (using patent data) with Luca Verginer, Federica Parisi and Prof. Massimo Riccaboni.  


From January 2018 until March I visited Prof. Tomaso Aste at UCL in London. From April 2018 until June I was at the Dutch central bank to collaborate with Prof. Iman van Lelyveld.


I hope to defend my thesis in February 2019, and I am open to new oppurtunities in industry in data science and network analysis.



Reconstructing mesoscale network structures - JVLDJ, R. Di Clemente, G. Caldarelli, F. Saracco and T. Squartini, Complexity, 10-01-2019
Detecting core-periphery structures by surprise - JVLDJ, G. Caldarelli and T. Squartini, arXiv preprint, 10-10-2018 [under review at EuroPhysics Letters]

The multilayer structure of corporate networks - JVLDJ, T. Aste and G. Caldarelli [08-01-2019 - second review round at New Journal of Physics]

Maximum entropy approach to link prediction in bipartite networks - JVLDJ, M. Baltakiene, K. Baltakys, D. Cardamone, F. Parisi, T. Radicioni, M. Torricelli, F. Saracco, arXiv preprint, 11-05-2018


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I have worked with Suzy Moat and Tobias Preis (Behavioural Science Group, Warwick Business School) in the Data Science Lab on quantifying the intentions of university applicants using Google Analytics
I was a research fellow in the Risk Initiative and Statistical Consulting Unit in the Department of Statistics at the University of Warwick. I joined the Department of Statistics in October 2014, working with Prof. Simon French and Prof. Robert MacKay (Centre for Complexity Science) on a joint project with an industrial partner, to implement regular analyses and reports on maintenance routines for UK utility company National Grid (Electricity Transmission Asset Management Directorate).

Before that I was a MSc student at the Centre for Complexity Science (University of Warwick), working on an optimization project with Juergen Branke (Operational Research & Management Sciences Group, Warwick Business School) and on forecasting using online behaviour with Suzy Moat and Tobias Preis (Behavioural Science Group, Warwick Business School). I have a background in applied physics at Delft University of Technology.

Other works

JVLDJ, Oyebolu, Folarin B., Siganporia, Cyrus, Farid, Suzanne S., Allmendinger, Richard and Branke, Jürgen. (2017) A new lot sizing and scheduling heuristic for multi-site biopharmaceutical productionJournal of Heuristics . ISSN 1381-1231
"Warwick Business School research project with data scientists Tobias Preis and Suzy Moat in the behavioural sciences group to see if Google Trends data can help to predict the future number of applications we have for our courses." - The Guardian (2014)