# ABRA: Approximating Betweenness Centrality in Static and Dynamic Graphs with Rademacher Averages

arXiv: https://arxiv.org/abs/1602.05866 · HTML: https://mlsanity.com/paper/1602.05866/

## Key facts

- Cited by 5 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 18 Feb 2016.

## Built on by (most cited first, 5 of 5)

- [Parallel and Distributed Graph Neural Networks: An In-Depth Concurrency Analysis](https://mlsanity.com/paper/2205.09702/) (2022)
- [Using Time-Aware Graph Neural Networks to Predict Temporal Centralities in Dynamic Graphs](https://mlsanity.com/paper/2310.15865/) (2023)
- [ONBRA: Rigorous Estimation of the Temporal Betweenness Centrality in Temporal Networks](https://mlsanity.com/paper/2203.00653/) (2022)
- [Degree-Mass Message Passing for Betweenness Ranking in Directed and Undirected Networks](https://mlsanity.com/paper/2602.09716/) (2026)
- [CLGNN: A Contrastive Learning-based GNN Model for Betweenness Centrality Prediction on Temporal Graphs](https://mlsanity.com/paper/2506.14122/) (2025)

## Abstract (arXiv)

We present ABRA, a suite of algorithms that compute and maintain probabilistically-guaranteed, high-quality, approximations of the betweenness centrality of all nodes (or edges) on both static and fully dynamic graphs. Our algorithms rely on random sampling and their analysis leverages on Rademacher averages and pseudodimension, fundamental concepts from statistical learning theory. To our knowledge, this is the first application of these concepts to the field of graph analysis. The results of our experimental evaluation show that our approach is much faster than exact methods, and vastly outperforms, in both speed and number of samples, current state-of-the-art algorithms with the same quality guarantees.