Mapping new models of complex data
Computational science has entered the era of Big Data, fueled by unparalleled amounts of data coming from high-throughput technologies and electronic records collected by various sensors and communication devices. The efficient exploitation of that data demands the development of more efficient computational methods as well as richer models to represent the salient features of the systems. In recent years, the most common language to describe such systems has been “Network Science”, where the organization of the system is summarized in terms of static nodes and edges, and dynamics modeled by Markov processes on an underlying network. During this abstracting step, several aspects of the data have been neglected: the nature of edges, the temporal patterns of activity of nodes and edges, pathways of information, etc. It is the goal and the fate of models to put away information. As long as this information is marginal, the model is a good one, but when the simplification neglects critical information, it needs to be improved. The main purpose of this talk is to focus on the following questions: When are simple network models sufficient and when are they not? What additional ingredients are needed to accurately model the dynamical processes? With access to more and more relational data, what are the most efficient ways to capture the structural information?
Renaud Lambiotte is assistant professor in the department of Mathematics. His recent research includes the development of algorithms to uncover information in large-scale networks, the study of empirical data in social and biological systems, and the mathematical modelling of human mobility and diffusion on networks.