reskit.features

reskit.features.bag_of_edges(X, SPL=None, symmetric=True, return_df=False, offset=1)[source]

Bag of edges graph metric.

Parameters:

X : numpy matrix

Adjacency matrix of a graph.

Returns:

bag_of_edges : numpy array

Bag of edges for the graph.

reskit.features.closeness_centrality(X)[source]

Closeness centrality graph metric.

Parameters:

X : numpy matrix

Adjacency matrix of a graph.

Returns:

cl_c : numpy array

Closeness centralities of nodes for the graph.

reskit.features.betweenness_centrality(X)[source]

Betweenness centrality graph metric.

Parameters:

X : numpy matrix

Adjacency matrix of a graph.

Returns:

btw : numpy array

Betweenness centralities of nodes for the graph.

reskit.features.eigenvector_centrality(X)[source]

Eigenvector centrality graph metric.

Parameters:

X : numpy matrix

Adjacency matrix of a graph.

Returns:

eigc : numpy array

Eigenvector centralities of nodes for the graph.

reskit.features.pagerank(X)[source]

Pagerank graph metric.

Parameters:

X : numpy matrix

Adjacency matrix of a graph.

Returns:

pgrnk : numpy array

Pagerank vector for the graph.

reskit.features.degrees(X)[source]

Degree graph metric.

Parameters:

X : numpy matrix

Adjacency matrix of a graph.

Returns:

degrees : numpy array

Degrees of nodes for the graph.

reskit.features.clustering_coefficient(X)[source]

Clustering coefficient graph metric.

Parameters:

X : numpy matrix

Adjacency matrix of a graph.

Returns:

clst_geommean : numpy array

Clustering coefficients of nodes for the graph.

reskit.features.triangles(X)[source]

Triangles graph metric.

Parameters:

X : numpy matrix

Adjacency matrix of a graph.

Returns:

tr : numpy array

Triangles of nodes for the graph.

reskit.features.efficiency(X)[source]

Efficiency graph metric.

Parameters:

X : numpy matrix

Adjacency matrix of a graph.

Returns:

efs : numpy array

Efficiency of nodes for the graph.