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.
Sample use cases
Social Media
Estimating a person’s popularity in social networks
Drug Discovery
Improving drug targeting by finding the control genes for specific diseases
Geospatial Data
Finding the optimal location of new public services for maximum accessibility
Machine Learning
Finding the most influential features for extraction in machine learning
Telecommunications
Identifying entities or people with a high amount of control over a network
Supply Chain
Assessing risk by spotting the highly influential pieces in the supply chain
