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We study the problem of learning the topology of an undirected network by observing a random subsample. Specifically, the sample is chosen by randomly selecting a fixed number of vertices, and for each we are allowed to observe all edges it is incident with. We analyze a general formalization of learning from such samples, and derive confidence bounds on the number of differences between the tr...

in general, the objective of distillation control is to maintain the desired products quality. in this paper, the performances of different one point control strategies for an aromatic distillation column have been compared through dynamic simulation. these methods are: a) composition control using measured composition directly. this method sufferes from large sampling delay of measuring device...

Tiziano Squartini, ∗ Giulio Cimini, 2 Andrea Gabrielli, 2 and Diego Garlaschelli IMT School for Advanced Studies, Piazza S.Francesco 19, 55100 Lucca Italy Istituto dei Sistemi Complessi ISC-CNR, via dei Taurini 15, 00185 Rome Italy Instituut-Lorentz for Theoretical Physics, Leiden Institute of Physics, University of Leiden, Niels Bohrweg 2, 2333 CA Leiden The Netherlands (Dated: October 21, 2016)

Sampling random graphs with given properties is a key step in the analysis of networks, as random ensembles represent basic null models required to identify patterns such as communities and motifs. A key requirement is that the sampling process is unbiased and efficient. The main approaches are microcanonical, i.e. they sample graphs that exactly match the enforced constraints. Unfortunately, w...

In this paper, we consider the problem of detecting an intruding packet in a communication network. Detection is accomplished by sampling a portion of the packets transiting selected network links (or router interfaces). Since sampling entails incurring network costs for real-time packet sampling and packet examination hardware, we would like to develop a network packet sampling strategy to eff...

Network sampling is integral to the analysis of social, information, and biological networks. Since many real-world networks are massive in size, continuously evolving, and/or distributed in nature, the network structure is often sampled in order to facilitate study. For these reasons, a more thorough and complete understanding of network sampling is critical to support the field of network sci...