# Betweenness Centrality

## Descriptionβ

Centrality analysis provides information about the nodeβs importance for an
information flow or connectivity of the network. Betweenness
centrality is one of the
most used centrality metrics. Betweenness centrality measures the extent to
which a node lies on paths between other nodes in the graph. Thus, nodes with
high **betweenness** may have considerable influence within a network under
their control over information passing between others. The calculation of
betweenness centrality is not standardized, and there are many ways to solve it.

It is defined as the **number of shortest paths** in the graph that passes
through the node divided by the total number of shortest paths. The implemented
algorithm is described in the paper "A Faster Algorithm for Betweenness
Centrality"
by Ulrik Brandes of the University of
Konstanz.

A larger circle means larger betweenness centrality. The middle one has the largest amount of shortest paths flowing through it.

## Materialsβ

### Implementationβ

Betweenness Centrality is implemented as part of the
**MAGE** project. Be sure to check it out in
the link above. βοΈ

### Blog postsβ

In recent years, researchers have proved that essential proteins often have a correlation with disease genes. Therefore, the identification of essential proteins is the foundation for disease diagnosis and treatment. Drug design methods heavily rely on detecting essential proteins to find drug targets.

### Playgroundβ

## Use casesβ

The metric of **betweenness centrality** can be used on a **macro** and
**micro** scale in social networks. On a macro scale, a high measure will
indicate the position of power (due to the large flow of information). On the
other hand, on a micro-scale, a high measure represents the strength of a
relationship with a narrow circle of friends.

**Protein-protein interaction
networks**
were created to get a better insight into how proteins interact with each other.

Maybe the most important usage of this algorithm is **transportation**. In a
complex and urban transportation
network, centrality
measurement can reveal the main bottlenecks and congestions within the system.
Such can help to organize the infrastructure of a big city, as well as the
amount spent in optimizing such routes.