# Exact and Approximate Algorithms for Computing Betweenness Centrality in Directed Graphs

@article{Chehreghani2021ExactAA, title={Exact and Approximate Algorithms for Computing Betweenness Centrality in Directed Graphs}, author={Mostafa Haghir Chehreghani and Albert Bifet and Talel Abdessalem}, journal={Fundam. Informaticae}, year={2021}, volume={182}, pages={219-242} }

Graphs (networks) are an important tool to model data in different domains. Realworld graphs are usually directed, where the edges have a direction and they are not symmetric. Betweenness centrality is an important index widely used to analyze networks. In this paper, first given a directed network G and a vertex r โ V (G), we propose an exact algorithm to compute betweenness score of r. Our algorithm pre-computes a set โ๐ฑ(r), which is used to prune a huge amount of computations that do notโฆย

## One Citation

Efficient Exact and Approximate Algorithms for Computing Betweenness Centrality in Directed Graphs

- Mathematics, Computer SciencePAKDD
- 2018

This paper proposes a new exact algorithm to compute betweenness score of r and shows that this algorithm significantly outperforms the most efficient existing randomized algorithms, in terms of both running time and accuracy.

## References

SHOWING 1-10 OF 42 REFERENCES

A faster algorithm for betweenness centrality

- Mathematics
- 2001

Motivated by the fastโgrowing need to compute centrality indices on large, yet very sparse, networks, new algorithms for betweenness are introduced in this paper. They require O(n + m) space and runโฆ

Centrality Estimation in Large Networks

- Mathematics, Computer ScienceInt. J. Bifurc. Chaos
- 2007

An experimental study of the quality of centrality scores estimated from a limited number of SSSP computations under various selection strategies for the source vertices is presented.

Dynamical algorithms for data mining and machine learning over dynamic graphs

- Computer ScienceWiley Interdiscip. Rev. Data Min. Knowl. Discov.
- 2021

Metropolis-Hastings Algorithms for Estimating Betweenness Centrality

- Computer ScienceEDBT
- 2019

This paper proposes a Metropolis-Hastings MCMC algorithm that samples from the space V (G) and estimates betweenness score of r and shows that the stationary distribution of the MCMC sampler is the optimal distribution and provides an (ฮต,ฮด )-approximation of the relative betweenness scores.

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

- Computer ScienceACM Trans. Knowl. Discov. Data
- 2018

ABRA, a suite of algorithms to compute and maintain probabilistically guaranteed high-quality approximations of the betweenness centrality of all nodes (or edges) on both static and fully dynamic graphs, is presented.

An In-depth Comparison of Group Betweenness Centrality Estimation Algorithms

- Computer Science2018 IEEE International Conference on Big Data (Big Data)
- 2018

This paper presents a generic algorithm that is used to express different approximate algorithms in terms of probability distributions, and presents an extension of distance-based sampling to group betweenness centrality, which is based on a new notion of distance between a single vertex and a set of vertices.

Efficient Exact and Approximate Algorithms for Computing Betweenness Centrality in Directed Graphs

- Mathematics, Computer SciencePAKDD
- 2018

This paper proposes a new exact algorithm to compute betweenness score of r and shows that this algorithm significantly outperforms the most efficient existing randomized algorithms, in terms of both running time and accuracy.

Improving the Betweenness Centrality of a Node by Adding Links

- Computer ScienceACM J. Exp. Algorithmics
- 2018

This article considers the problem of determining how much a vertex can increase its centrality by creating a limited amount of new edges incident to it and proposes a simple greedy approximation algorithm for MBI with an almost tight approximation ratio and test its performance on several real-world networks.

Two-level clustering fast betweenness centrality computation for requirement-driven approximation

- Computer Science2017 IEEE International Conference on Big Data (Big Data)
- 2017

An algorithm for computing approximated values of betweenness that allows for tuning its performance on the basis of a tolerable error is proposed, aimed at reducing the number of single-source shortest-paths explorations via a pivot-based technique that exploits topological properties of graphs and clustering.

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

- Computer Science, MathematicsKDD
- 2016

We present ABRA, a suite of algorithms to compute and maintain probabilistically-guaranteed, high-quality, approximations of the betweenness centrality of all nodes (or edges) on both static andโฆ