Package index • snafun

Create and manipulate graph objects

Create graph objects

create_bipartite(): Create a random bipartite network

create_census_graph(): Create a Dyad Census-Conditioned Random Graph

create_community_graph(): Create a graph with a given community structure

create_components_graph(): Create a graph with strict components

create_empty_graph(): Create an empty network

create_manual_graph(): Create a manual (literal) graph

create_random_graph(): Create a random bipartite network

Remove parts of the graph

remove_edge_attribute() remove_edge_weight() remove_vertex_attribute() remove_graph_attribute() remove_vertex_names(): Remove parts of the graph

remove_isolates(): Remove all isolates from the graph

remove_loops(): Remove loops

remove_vertices(): Remove vertices

Add elements to the graph

add_edge_attributes(): Add edge attributes to a graph

add_graph_attribute(): Add a graph attribute

add_vertex_attributes(): Add vertex attributes to a graph

add_vertex_names(): Add vertex names to a graph

Extract parts of the graph

Functions starting with extract_*. The functions that contain _comm_ extract community structures from the graph.

extract_vertex_attribute() extract_vertex_names() extract_edge_attribute() extract_graph_attribute(): Extract attributes from the graph object

extract_all_vertex_attributes(): Extract all vertex attributes

extract_comm_fastgreedy(): Community structure via greedy optimization of modularity

extract_comm_girvan(): Community structure based on edge betweenness

extract_comm_louvain(): Community structure via multi-level optimization of modularity

extract_comm_walktrap(): Community structure via short random walks

extract_edge_id(): Extract edge id's

extract_egonet(): Extract ego networks

extract_intersection(): Intersection of graphs

extract_isolates(): Extract the isolates

extract_loops() extract_loops_vertex(): Extract loops

extract_neighbors(): Extract the neighbors of a vertex

extract_subgraph(): Extract a subgraph

List parts of the graph

list_vertex_attributes() list_edge_attributes() list_graph_attributes(): List the attributes in the graph object

Indicators

Indicators of specific traits of the graph, returning TRUE or FALSE

has_edge_attributes() has_vertex_attributes() has_vertex_attribute() has_edge_attribute() has_vertexnames() has_loops() has_isolates(): Check for the existence of edge attributes in the graph

is_bipartite(): Is the network bipartite?

is_connected(): Is the network connected?

is_directed(): Is the network directed?

is_signed(): Is the network signed?

is_network() is_igraph(): Is something?

is_weighted(): Is the network weighted?

Make new objects from the graph object

Use (info from) the graph object(s) to construct some useful output object

make_edgelist(): Make an edgelist from row data

make_matrix_from_vertex_attribute(): Make a matrix from a vertex attribute

make_mixingmatrix(): Construct a mixing matrix

make_nodelist(): Make a node list from row data

make_perturbed_graph(): Perturb a binary graph

make_union(): Union of graphs

Conversion between graph classes

to_binary_matrix(): Dichotomize a matrix

to_edgelist(): Make edgelist

to_igraph(): Make igraph object

to_matrix(): Make a matrix from a graph

to_network(): Make network object

to_symmetric_matrix(): Symmetrize an adjacency matrix

Some additional graph manipulation

contract_vertices(): Contract multiple vertices into 1 new vertex

merge_membership(): Merge community membership

Display the graph object

print(<igraph>) print(<network>): Print the graph object

Network measures

Graph-level indices

Indices at the level of the graph itself Functions starting with count_* provide a count of specific greaph-level attributes. Functions starting with g_* calculate common graph-level indices, such as mean distance, centralization, or reciprocity.

g_summary() g_density() g_mean_distance() g_correlation() g_reciprocity() g_transitivity() g_diameter() g_radius() g_compactness() g_degree_distribution(): Graph level indices

count_dyads(): Count dyad types

count_edges(): Number of edges in the graph

count_edges_in_interval(): Count edges in time interval

count_triads(): Count triad types

count_unique_edges_in_interval(): Count unique edges in time interval

count_vertices(): Number of vertices in the graph

g_centralize(): Centralization

g_efficiency(): Compute Graph Efficiency

g_secrecy() v_secrecy(): secrecy index

g_vuln_attack(): Vulnerability: attack

g_vuln_efficiency(): Vulnerability: efficiency

g_vuln_paths(): Vulnerability: paths

Edge-level indices

Indices at the level of the dyads (directed or undirected) (Almost) all functions start with d_*

d_distance(): Dyad level indices

d_structural_equivalence(): Structural equivalence

Vertex-level indices

Indices at the level of the vertices

v_degree() v_eccentricity() v_betweenness() v_stress() v_eigenvector() v_closeness() v_harmonic() v_pagerank() v_geokpath() v_shapley(): Vertex level indices

g_secrecy() v_secrecy(): secrecy index

v_bottleneck(): Find the Bottleneck centrality score

v_distance(): Distances to and from a vertex

v_geokpath_w(): Geodesic k-path centrality

Tools for statistical modeling

fix_cug_input(): Fix intermediate CUG input

stat_ef_int(): Estimate the intensity of effects using odds ratio and probabilities

stat_nam(): Network autocorrelation model

stat_nam_summary(): Network autocorrelation results

stat_plot_gof(): Plot the Goodness of fit of a (bt)ergm result

stat_plot_gof_as_btergm(): gof and plot (btergm style)

plot_nam(): Plot diagnostics for the network autorrelation model

Plotting

Generic functions to plot graph objects. Also, functions to plot features of the graph or output from statistical network models.

plot(<igraph>) plot(<network>): Plot the graph object

plot_centralities(): Plot one or more centrality scores

plot_comm_dendrogram(): Plot a dendrogram of the community structure

plot_nam(): Plot diagnostics for the network autorrelation model

plot_network_slices(): Plot network slices

Datasets

A small set of datasets, mainly used in examples or testing. A more interesting set of data for the SNA4DS course can be found in the SNA4DSData package.

florentine: Florentine families

judge_net: Judges network

Madrid_bombing: Madrid Train Bombing 2004 (dataset)

soccer98: Soccer98 network