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Adjacency matrix of the graph. 

Yields all shortest paths from source to target. 

Returns the assortativity of the graph. 

Returns the average shortest path length between sources and targets. 

Bayesian Blocks Implementation 

Returns the normalized betweenness centrality of the nodes and edges. 

Betweenness distribution of a graph. 

Binning function providing automatic binning using Bayesian blocks in addition to standard linear and logarithmic uniform bins. 

Returns the closeness centrality of some nodes. 

Returns the connected component to which each node belongs. 

Degree distribution of a graph. 

Returns the diameter of the graph. 

Return the B2 coefficient for the neurons. 

Return the average firing rate for the neurons. 

Return a 2D sparse matrix, where: 

Returns the global clustering coefficient. 
Returns the undirected global clustering coefficient. 


Compute the local closure for each node, as defined in [Yin2019] as the fraction of 2walks that are closed. 

Local (weighted directed) clustering coefficient of the nodes, ignoring selfloops. 
Returns the undirected local clustering coefficient of some nodes. 


Return node attributes for a set of nodes. 

Returns the number of inhibitory connections. 
Calculate the edge reciprocity of the graph. 


Returns the length of the shortest paths between sources`and `targets. 

Returns a shortest path between source`and `target. 

Returns the smallworld propensity of the graph as first defined in [Muldoon2016]. 

Spectral radius of the graph, defined as the eigenvalue of greatest module. 

Returns the subgraph centrality for each node in the graph. 

Computes the total firing rate of the network from the spike times. 

Same as 

Returns the number or the strength (also called intensity) of triangles for each node. 

Returns the number or the strength (also called intensity) of triplets for each node. 