Graph Theory - Indicators of Centrality and Ultipa Graph Database
Yuri Simione
VP of Global Partnerships & Alliances at Ultipa | Seeking ideal partners to collaboratively grow with the fastest, most scalable real-time graph database and analytics platform.
In graph theory and network analysis, centrality indicators assign numbers or rankings to nodes within a graph based on their position in the network. Applications include identifying the most influential people in a social network, key infrastructure nodes in Internet or urban networks, disease super-spreaders and brain networks. The concepts of centrality were first developed in social network analysis, and many of the terms used to measure centrality reflect their sociological origins.
Ultipa graph database offers more than 100 of algorithms for graph analysis, many of which can be run in real time. One of these algorithms is the one that allows the calculation of betweenness centrality.
In graph theory, betweenness centrality is a measure of the centrality of a graph based on minimum paths. For each pair of nodes in a connected graph, there is at least one shortest path between the nodes such that the number of edges crossed by the path (for unweighted graphs) or the sum of the weights of the edges (for weighted graphs) is minimized. The betweenness centrality for each node is the number of these shortest paths that pass through the node. The most elegant definition of betweenness centrality is the one found in the official documentation of our graph database, in the section on graph analysis algorithms:
Betweenness centrality is used to measure the probability that a node appears in the shortest paths between any other two nodes. Proposed by Linton C. Freeman in 1977, this concept can effectively calculate the nodes that play an important role as bridge and medium between multiple parts of the graph.
Betweenness centrality has wide application in network theory; it represents the degree of interdependence between nodes. For example, in a telecommunications network, a node with higher betweenness centrality will have more control over the network because more information will pass through it.
Want to learn more about this algorithm? In this short video created by our team, you will learn about the necessary theory and also see a practical application using Ultipa Manager.
Among other algorithms that calculate centrality indicators and available in Ultipa Graph Database, we find (and at least one of them we use every day, dozens of times a day!):
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