Unbiased sampling of network ensembles

Unbiased sampling of network ensembles

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...

Sampling Ensembles for Frequent Patterns

A popular solution to improving the speed and scalability of association rule mining is to do the algorithm on a random sample instead of the entire database. But it is at the expense of the accuracy of answers. In this paper, we present a sampling ensemble approach to improve the accuracy for a given sample size. Then, using Monte Carlo theory, we give an explanation for a sampling ensemble an...

Unbiased Sampling over Online Social Networks

During recent years, Online Social Networks (OSNs) have evolved in many ways and attracted millions of users. The dramatic increase in the popularity of OSNs has encouraged network researchers to examine their connectivity structure. The majority of empirical studies for characterizing OSN connectivity graphs have analyzed snapshots of the system taken in different times. These snapshots are co...

Compressive Sampling of Ensembles of Correlated Signals

We propose several sampling architectures for the efficient acquisition of an ensemble of correlated signals. We show that without prior knowledge of the correlation structure, each of our architectures (under different sets of assumptions) can acquire the ensemble at a sub-Nyquist rate. Prior to sampling, the analog signals are diversified using simple, implementable components. The diversific...

Analysis of Network Ensembles