Automated Modelling Complex Systems

Building models for complex systems depends on professional experience which is very hard to be generalized. How can we build models for complex systems according to the behaviors of the system in an automatic way? As new techniques like Graph Network, Deep Learning, and other techniques, we can reconstruct the dynamical models from time series data. We have developed our own models on data-driven modelling of complex systems. Our models can not only learn the dynamics and make predictions, but also can reaveal the hidden network structures hehind the system.


Machine Learning on Complex Systems

Machine learning provides us new tools to handle complexity in big data and solve hard problems. For example, we have applied CapsuleNet model on single-cell RNA-sequencing data to identify cell types. We also applied convolutional network on complex network classifications.


Open Flow Networks

Flows are ubiquitous in open complex systems. For example, energy flow, money flow, material flow, etc. However, there is no appropriate tool to analyze these flows on open systems. We develop one of the network analysis from ecology, which is called open flow networks. By modeling a flow system as a directed weighted network, we can depict distributions and structures of flows in a system. By adding two special nodes, source and sink, into the network, we can model open systems. We have applied this method on different fields including, online education and learning, online forum, web sites, international trading system, food webs, etc.


Scaling Analysis

Many complex systems exhibit universal scaling laws, for example, the famous Kleiber law in biology, and the scaling law in cities. On one side, by discovering scaling laws on the aggregated level from the data, scaling analysis can characterize the macroscopic universal patterns of complex systems, on the other side, scaling laws can provide some insights for the micro-level mechanisms. I have done several works on scaling laws, for example, in urban system, firms, web forums, and so on.



Research Group


School of Systems Science
Beijing Normal University, 100875
Beijing, China