Hypergraph Properties
HAT implements computations for a series of standard graph and hypergraph theoretic properties. These properties are computed on either hypergraph or graph objects, which are created based on incidence and adjacency matrices respectively.
H = HAT.hypergraph.hypergraph(W) # Create a hypergraph with an incidence matrix W
G = HAT.graph.graph(A) # Create a graph with an adjacency matrix A
Diameter
The diameter is defined as the maximum minimum distance between any two vertices in a graph or hypergraph.
diameter = H.diameter
Clustering Coefficient
The clustering coefficient of a graph or hypergraph is the average clustering coefficient of all vertices. For any given vertex, the vertex clustering coefficient is calculated as
gamma = H.clusteringCoefficient # Hypergraph clustering coeffficient
gammaI = H.clusteringCoefficient(i) # Clustering coefficient of vertex i
Average Distance
The average distance is the pairwise distance between any two vertices.
avgDistane = H.averageDistance
Diameter
Clustering Coefficient
Average Distance
etc.