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Adjacency matrix of the graph. |
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Yields all shortest paths from source to target. |
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Returns the assortativity of the graph. |
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Returns the average shortest path length between sources and targets. |
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Bayesian Blocks Implementation |
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Returns the normalized betweenness centrality of the nodes and edges. |
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Betweenness distribution of a graph. |
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Binning function providing automatic binning using Bayesian blocks in addition to standard linear and logarithmic uniform bins. |
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Returns the closeness centrality of some nodes. |
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Returns the connected component to which each node belongs. |
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Degree distribution of a graph. |
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Returns the diameter of the graph. |
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Return the B2 coefficient for the neurons. |
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Return the average firing rate for the neurons. |
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Return a 2D sparse matrix, where: |
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Returns the global clustering coefficient. |
Returns the undirected global clustering coefficient. |
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Compute the local closure for each node, as defined in [Yin2019] as the fraction of 2-walks that are closed. |
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Local (weighted directed) clustering coefficient of the nodes, ignoring self-loops. |
Returns the undirected local clustering coefficient of some nodes. |
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Return node attributes for a set of nodes. |
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Returns the number of inhibitory connections. |
Calculate the edge reciprocity of the graph. |
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Returns the length of the shortest paths between sources`and `targets. |
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Returns a shortest path between source`and `target. |
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Returns the small-world propensity of the graph as first defined in [Muldoon2016]. |
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Spectral radius of the graph, defined as the eigenvalue of greatest module. |
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Returns the subgraph centrality for each node in the graph. |
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Computes the total firing rate of the network from the spike times. |
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Same as |
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Returns the number or the strength (also called intensity) of triangles for each node. |
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Returns the number or the strength (also called intensity) of triplets for each node. |