Algorithms

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Centrality measures (networksns.centrality_measures)

centrality_measures.total_communicability(G)	Computes the total communicability of all the nodes of a graph \(G\).
centrality_measures.node_total_communicability(G, u)	Computes the node total communicability of node \(u\).
centrality_measures.total_network_communicability(G)	Computes the total network communicability of \(G\).
centrality_measures.edge_total_communicability(G, u, v)	Computes the edge total communicabilities of edge \((u, v)\).
centrality_measures.subgraph_centrality(G[, ...])	Computes the subgraph centrality of all the nodes in graph \(G\).
centrality_measures.node_subgraph_centrality(G, u)	Computes the subgraph centrality of node \(u\).
centrality_measures.total_directed_communicability(G)	Computes the total hub communicability and the total authority communicability of a directed graph \(G\).
centrality_measures.node_total_directed_communicability(G, u)	Computes the total hub and authority communicability of node \(u\).
centrality_measures.directed_subgraph_centrality(G)	Computes the hub and the authority centrality of all nodes in a directed graph \(G\).
centrality_measures.node_directed_subgraph_centrality(G, u)	Computes the hub and the authority centrality of node \(u\).
centrality_measures.broadcast_centrality(G)	Computes the broadcast centrality of the dynamic graph \(G\).
centrality_measures.receive_centrality(G[, ...])	Computes the receive centrality of the dynamic graph \(G\).
centrality_measures.approximated_broadcast_centrality(G)	Computes an approximated version of the broadcast centrality.
centrality_measures.approximated_receive_centrality(G)	Computes an approximated version of the receive centrality.
centrality_measures.trip_centrality(...[, ...])	Computes the trip centrality of a temporal multiplex.
centrality_measures.betweenness_centrality(...)	Computes the betweenness centrality of a temporal multiplex.
centrality_measures.exponential_symmetric_quadrature(A, u)	Computes \(q=u^Te^Au\).
centrality_measures.exponential_quadrature(A, u, v)	Computes \(q=u^T e^A v\).
centrality_measures.graph_slice(G, t)	extract a slice/snapshot of the Dynamic graph, that is a snapshot of the graph \(G\) at time \(t\) in NetworkX format.

Statistical models (networksns.statistical_models)

statistical_models.darn_simulation(p, Q, Y, n, s)	Simulate a temporal network following the \(DARN(p)\) model.
statistical_models.darn(time_series, p[, ...])	Estimate, by maximum likelihood method, the parameters of the \(DARN(p)\) model.
statistical_models.cdarn_simulation(p, Q, c, ...)	Simulate a temporal network following the \(CDARN(p)\) model.
statistical_models.cdarn(time_series, p, B)	Simulate a temporal network following the \(CDARN(p)\) model.
statistical_models.tgrg_simulation(n, T[, ...])	Simulate a temporal network following the Temporally Generalized Random Graph model (\(TGRG\)).
statistical_models.tgrg(time_series[, tol, ...])	Estimate by an expectation-maximization algorithm the parameters of the Temporally Generalized Random Graph model (\(TGRG\)).
statistical_models.tgrg_directed_simulation(n, T)	Simulate a directed temporal network following the Temporally Generalized Random Graph model (\(TGRG\)).
statistical_models.tgrg_directed(time_series)	Estimate, by an expectation-maximization algorithm, the parameters of the Temporally Generalized Random Graph model (\(TGRG\)).
statistical_models.dar_tgrg_simulation(n, T)	Simulate a temporal network following the Discrete Auto-Regressive Temporally Generalized Random Graph model (\(DAR\)-\(TGRG\)).
statistical_models.dar_tgrg(time_series[, ...])	Estimate, by an expectation-maximization algorithm, the parameters of the Discrete Auto-Regressive Temporally Generalized Random Graph model (\(DAR\)-\(TGRG\)).
statistical_models.dar_tgrg_directed_simulation(n, T)	Simulate a directed temporal network following the Discrete Auto-Regressive Temporally Generalized Random Graph model (\(DAR\)-\(TGRG\)).
statistical_models.dar_tgrg_directed(time_series)	Estimate, by an expectation-maximization algorithm, the parameters of the Discrete Auto Regressive Temporally Generalized Random Graph model (\(DAR\)-\(TGRG\)).