Better understanding of complex networks
To understand complex systems, it is usually not enough to analyse their constituent parts. Complex networks are not uniform and are usually composed of different groups that interact in a dynamic way. The EU-funded GRODYNET (Group-based dynamics on complex networks) project has developed a general theoretical framework that could be utilised to understand the interplay between network structure and system dynamics. Long-term, the goal is to apply this framework to biological and socioeconomic systems of interest. Using group-based inference models to predict the evolution of networks, GRODYNET researchers have extended these tools for systems that are not network systems per se, such as for predicting human behaviour and decisions. They have also confirmed the importance of group rather than individual nodes such as a group of films on network dynamics. Interestingly, even setting aside domain knowledge, which in the case of court decisions would be the judiciary systems, the inference models developed are more specific than domain-specific methods. This same model has been applied to understand cell polarity in yeast and delineation of the meso-scale interactions of proteins implicated in the network. The researchers demonstrated that a model with a single kinetic constant for each group of proteins is capable of predicting the dynamics of individual proteins. Applications for the GROYDYNET model cover a vast range in many important areas, including the biomedical arena and healthcare. Migration of cancer cells in metastasis, predicting interactions between drugs in personalised medicine and the fight against microbial resistance to drugs all come under this systems umbrella.
Keywords
Complex networks, complex systems, group-based dynamics, system dynamics, inference models
