Network inference with confidence from multivariate time series

Network inference with confidence from multivariate time series.

Networks--collections of interacting elements or nodes--abound in the natural and manmade worlds. For many networks, complex spatiotemporal dynamics stem from patterns of physical interactions unknown to us. To infer these interactions, it is common to include edges between those nodes whose time series exhibit sufficient functional connectivity, typically defined as a measure of coupling excee...

Network structure of multivariate time series

Our understanding of a variety of phenomena in physics, biology and economics crucially depends on the analysis of multivariate time series. While a wide range tools and techniques for time series analysis already exist, the increasing availability of massive data structures calls for new approaches for multidimensional signal processing. We present here a non-parametric method to analyse multi...

Neural network structure inference from its time series

I explore the task of neural network connectivity (i.e. graph) inference from a set of temporal measurements at each neuron (nodes). In this work, all experiments are based on numerically simulated neuronal populations, with known ground-truth graph structures. I develop a statistically principled framework for edge inference based on the concept of “false discovery rate” (FDR) as outlined in R...

Inference on BitTorrent Mainline DHT from Network Evolution Time Series

Network size is a fundamental statistic for a peer-to-peer system and is generally considered to contain valuable information for analyzing system dynamics. BitTorrent Mainline DHT, as the largest peer-to-peer system today, has been attracting more and more attention in the research community because it provides us an excellent opportunity to study the global user behavior in a content distribu...

Methods in Brain Connectivity. Inference through multivariate time series analysis