# SILVAN: Estimating Betweenness Centralities with Progressive Sampling and Non-uniform Rademacher Bounds

@article{Pellegrina2021SILVANEB, title={SILVAN: Estimating Betweenness Centralities with Progressive Sampling and Non-uniform Rademacher Bounds}, author={Leonardo Pellegrina and Fabio Vandin}, journal={ArXiv}, year={2021}, volume={abs/2106.03462} }

Betweenness centrality is a popular centrality measure with applications in several domains, and whose exact computation is impractical for modern-sized networks. We present SILVAN, a novel, efficient algorithm to compute, with high probability, accurate estimates of the betweenness centrality of all nodes of a graph and a high-quality approximation of the top- π betweenness centralities. SILVAN follows a progressive sampling approach, and builds on novel bounds based on Monte-Carlo Empiricalβ¦Β

## 2 Citations

### ONBRA: Rigorous Estimation of the Temporal Betweenness Centrality in Temporal Networks

- Computer ScienceWWW
- 2022

This work presents, the first sampling-based approximation algorithm for estimating the temporal betweenness centrality values of the nodes in a temporal network, providing rigorous probabilistic guarantees on the quality of its output.

### Bounding the Family-Wise Error Rate in Local Causal Discovery Using Rademacher Averages

- Computer ScienceECML/PKDD
- 2022

This paper introduces a novel algorithm for the MB discovery problem with rigorous guarantees on the Family-Wise Error Rate (FWER), that is, the probability of reporting any false positive in the Markov boundary.

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