Weighted and Directed Networks

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Documentation for package ‘wdnet’ version 0.0.4

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+.rpactl Add components to the control list
adj_to_edge Convert adjacency matrix to edgelist and edgeweight.
assortcoef Assortativity coefficient
centrality Centrality measures
closeness_c Closeness centrality
clustcoef Directed clustering coefficient
cvxr.control Parameters passed to CVXR::solver().
degree_c Degree-based centrality
dprewire Degree preserving rewiring.
dprewire.range Range of assortativity coefficient.
dprewire_directed Degree preserving rewiring for directed networks
dprewire_directed_cpp Degree preserving rewiring process for directed networks.
dprewire_undirected Degree preserving rewiring for undirected networks
dprewire_undirected_cpp Degree preserving rewiring process for undirected networks.
dw_assort Compute the assortativity coefficient of a weighted and directed network.
dw_feature_assort Feature based assortativity coefficient
edge_to_adj Convert edgelist and edgeweight to adjacency matrix.
fill_weight_cpp Fill edgeweight into the adjacency matrix. Defined for function 'edge_to_adj'.
find_node_cpp Fill missing nodes in the node sequence. Defined for 'wdnet::rpanet'.
find_node_undirected_cpp Fill missing values in node sequence. Defined for 'wdnet::rpanet'.
get_constr Get the constraints for the optimization problem. This function is defined for 'get_eta_directed'.
get_dist Get the node-level joint distributions and some empirical distributions with given edgelist.
get_eta_directed Compute edge-level distributions for directed networks with respect to desired assortativity level(s).
get_eta_undirected Compute edge-level distribution for undirected networks with respect to desired assortativity level.
get_values Get the value of an object from the optimization problem. This function is defined for 'get_eta_directed'.
node_strength_cpp Aggregate edgeweight into nodes' strength.
rpactl.edgeweight Set parameters for controlling weight of new edges
rpactl.newedge Set parameters for controlling new edges in each step
rpactl.preference Set parameters for source and target preference function
rpactl.reciprocal Set parameters for controlling reciprocal edges
rpactl.scenario Set parameters for controlling the probability of edge scenarios
rpanet Generate PA networks.
rpanet_general Generate a PA network with non-linear preference functions
rpanet_nodelist_cpp Preferential attachment algorithm for simple situations, e.g., edge weight equals to 1, number of new edges per step is 1.
rpanet_simple Generate a PA network with linear preference functions.
rpanet_wan Simulating a Preferential Attachment Network
sample_node_cpp Uniformly draw a node from existing nodes for each time step. Defined for 'wdnet::rpanet'.
wdnet wdnet: Weighted and Directed Networks
wpr Weighted PageRank centrality