Relational inference leverages relationships between entities and links in a network to infer information about the network from a small sample. This method is often used when global information about the network is not available or difficult to obtain. However, how reliable is inference from a small labelled sample? How should the network be sampled, and what effect does it have on inference e...

Sampling techniques play a key role in achieving efficient network measurements by reducing the amount of traffic processed while trying to maintain the accuracy of network statistical behavior estimation. Despite the evolution of current techniques regarding the correctness of network parameters estimation, the overhead associated with the volume of data involved in the sampling process is sti...

Researchers are increasingly turning to network theory to describe and understand the social nature of animal populations. To make use of the statistical tools of network theory, ecologists need to gather relational data, typically by sampling the social relations of a population of animals over a given time-period. Due to effort constraints and the practical difficulty involved in tracking ani...

Multimedia and real-time services are spreading all over the Internet. The delivery quality of such contents is closely related to its network performance, for example in terms such as low latency or few packet losses. Both customers and operators want some feedback from the network in order to assess the real provided quality. There are proposals about distributed infrastructures for the on-li...

Many models and methods are now available for network analysis, but model selection and tuning remain challenging. Cross-validation is a useful general tool for these tasks in many settings, but is not directly applicable to networks since splitting network nodes into groups requires deleting edges and destroys some of the network structure. Here we propose a new network cross-validation strate...

Network sampling and classical survey sampling differ with respect to the counting rule paradigm for linking population elements to the selection units at which they are countable in the survey [20]. Classical survey sampling uses unitary counting rules, such as de jure and de facto residence rules in household surveys, that seek to uniquely link each person to one and only household. Network s...

Available bandwidth knowledge is very useful to network protocols. Unfortunately, available bandwidth is also very difficult to measure in packet networks, where methods to guarantee and keep track of the bandwidth (eg, weighted fair queuing scheduling) do not work well, for example the Internet. In this paper we are dealing with an available bandwidth sampling technique based on the observatio...

Network embedding methods have garnered increasing aŠention because of their e‚ectiveness in various information retrieval tasks. Œe goal is to learn low-dimensional representations of vertexes in an information network and simultaneously capture and preserve the network structure. Critical to the performance of a network embedding method is how the edges/vertexes of the network is sampled for ...

Edge sampling is an important topic in network analysis. It provides a natural way to reduce network size while retaining desired features of the original network. Sampling methods that only use local information are common in practice as they do not require access to the entire network and can be parallelized easily. Despite promising empirical performance, most of these methods are derived fr...

The Teacher Forcing algorithm trains recurrent networks by supplying observed sequence values as inputs during training and using the network’s own one-stepahead predictions to do multi-step sampling. We introduce the Professor Forcing algorithm, which uses adversarial domain adaptation to encourage the dynamics of the recurrent network to be the same when training the network and when sampling...