Information Filtering Networks

We are witnessing interesting times rich of information, readily available for us all. Using, understanding and filtering such information has become a major activity across science, industry and society at large. I will show how networks built from similarity/distance measures, both linear and non-linear, can be used to process information while it is generated reducing complexity and dimensionality of the dataset. I’ll describe different algorithms to build these networks and I’ll discuss bounds on information retrieval. Applications to financial systems will demonstrate how reliable and meaningful models can be constructed making use of these information filtering networks.