Centrality algorithms in Neptune Analytics
Centrality algorithms utilize the topology of a network to determine the relative importance or influence of a specific node within the graph. By measuring the relative importance of a node or edge within a network, centrality values can indicate which elements in a graph play a critical role in that network.
By identifying the most influential or important nodes within a network, centrality algorithms can provide insights about key players or critical points of interaction. This is valuable in social network analysis, where it helps pinpoint influential individuals, and in transportation networks, where it aids in identifying crucial hubs for efficient routing and resource allocation.
Different types of centrality algorithms use different techniques to measure the importance of a node. Understanding how an algorithm calculates centrality is important to understanding the meaning of its outputs.
In addition to returning centrality data to the client, Neptune Analytics provides mutate variations of the centrality algorithms which store the calculated centrality values as vertex properties in the graph.
Neptune Analytics supports three centrality algorithms along with their mutate variants:

degree – This measures a nodes's centrality by the number of edges connected to it, and can therefore be used to find the most connected nodes in a network.

degree.mutate – The degree centrality mutate algorithm measures the number of incident edges of each vertex it traverses and writes that calculated degree value as a property of the vertex.

pageRank – This is an iterative algorithm that measures a nodes's centrality by the number and quality of incident edges and adjacent vertices. The centrality of a node connected to a few important nodes may therefore be higher than that of a node connected to many less important nodes. The output of this algorithm is a value that indicates the importance of a given node relative to the other nodes in the graph.

pageRank.mutate – This algorithm stores the calculated PageRank of a given node as a property of the node.

closenessCentrality – This algorithm computes the closeness centrality (CC) metric of nodes in a graph. The closeness centrality metric of a vertex is a positive measure of how close it is to all other vertices, or how central it is in the graph. Because it indicates how quickly all other nodes in a network can be reached from a given node, it can be used in transportation networks to identify key hub locations, and in diseasespread modeling to pinpoint central locations for targeted intervention efforts.

closenessCentrality.mutate – This algorithm computes the closeness centrality (CC) metric of vertices in a graph and writes them as a property of each vertex.