Graph Algorithms in SQL
Betweenness Centrality
Betweenness centrality is a way of detecting the amount of influence a node has over the flow of information in a graph. The algorithm calculates the shortest paths between all pairs of nodes in a graph. Each node receives a score, based on the number of shortest paths that pass through the node. Nodes that more frequently lie on shortest paths between other nodes will have higher betweenness centrality scores.
![](https://timbr.ai/wp-content/uploads/2021/11/Betweenness-Centrality-1024x268.jpg)
Sample use cases
![](https://timbr.ai/wp-content/uploads/2021/11/social-4.png)
Social Media
Estimating a person’s popularity in social networks
![](https://timbr.ai/wp-content/uploads/2021/11/Drug-2.png)
Drug Discovery
Improving drug targeting by finding the control genes for specific diseases
![](https://timbr.ai/wp-content/uploads/2021/11/GEO-5.png)
Geospatial Data
Finding the optimal location of new public services for maximum accessibility
![](https://timbr.ai/wp-content/uploads/2021/11/ML.png)
Machine Learning
Finding the most influential features for extraction in machine learning
![](https://timbr.ai/wp-content/uploads/2021/11/Telecommunications-2.png)
Telecommunications
Identifying entities or people with a high amount of control over a network
![](https://timbr.ai/wp-content/uploads/2021/11/supply-chain-1.png)
Supply Chain
Assessing risk by spotting the highly influential pieces in the supply chain