Hydrus calculates 4 different centrality metrics to determine how cardinal is a node in the network: degree, betweenness, closeness and eigenvector.
Degree centrality is a measure to determine the importance of a node within a graph.
It quantifies how connected a node is by counting the number of channels it has to other nodes. A higher degree centrality indicates that a node is more central or influential within the network.
In a social network graph, if node A has 10 channels, and node B has 2, then node A would have a higher degree centrality than node B, making it more central in the network.
Betweenness centrality is a measure to determine the importance of a node based on its role in connecting different parts of the network.
Unlike degree centrality, which counts the number of direct connections a node has, betweenness centrality focuses on how often a node lies on the shortest paths between pairs of other nodes.
Nodes with high betweenness centrality act as bridges, connecting different parts of the network.
Closeness centrality quantifies how centrally located a node is within a graph by measuring its average proximity to all other nodes.
Closeness centrality for a node A is the reciprocal of the sum of the shortest path distances from A to all other reachable nodes in the network.
The Eigenvector centrality is a measure to determine the influence or importance of nodes within a network, the score is calculated based on the principle that if one node has a high centrality, then all of its neighbors should also have high centralities.