Seminars & Colloquia Calendar

  Abstract:  We propose a new theoretical methodology for the inference of statistical properties of complex networks with weighted or unweighted edges. The statistics of interest include, but are not limited to, the node degree distribution, the average degree of nearest-neighbor nodes, and the node clustering coefficient. Our formalism yields high-accuracy estimates of these statistics and of the network size, after only a small fraction of network nodes has been explored. The Bayesian nature of our approach provides rigorous estimates of all parameter uncertainties. First, we demonstrate our framework on several standard examples, including random, scale-free, and small-world networks, and apply it to the real-world network formed by the links between Wikipedia pages. Second, we present preliminary results related to exploring evolving networks, networks with community structure, and dynamic processes on networks.
We focus on the application of our method to the propagation of infectious diseases on contact networks, and to obtaining census-type population data from small samples.