Functions#

Graph#

degree(G[, nbunch, weight])

Returns a degree view of single node or of nbunch of nodes.

degree_histogram(G)

Returns a list of the frequency of each degree value.

density(G)

Returns the density of a graph.

info(G[, n])

Return a summary of information for the graph G or a single node n.

create_empty_copy(G[, with_data])

Returns a copy of the graph G with all of the edges removed.

is_directed(G)

Return True if graph is directed.

to_directed(graph)

Returns a directed view of the graph graph.

to_undirected(graph)

Returns an undirected view of the graph graph.

is_empty(G)

Returns True if G has no edges.

add_star(G_to_add_to, nodes_for_star, **attr)

Add a star to Graph G_to_add_to.

add_path(G_to_add_to, nodes_for_path, **attr)

Add a path to the Graph G_to_add_to.

add_cycle(G_to_add_to, nodes_for_cycle, **attr)

Add a cycle to the Graph G_to_add_to.

subgraph(G, nbunch)

Returns the subgraph induced on nodes in nbunch.

induced_subgraph(G, nbunch)

Returns a independent deep copy subgraph induced on nbunch.

edge_subgraph(G, edges)

Returns a independent deep copy subgraph induced by the specified edges.

Nodes#

nodes(G)

Returns an iterator over the graph nodes.

number_of_nodes(G)

Returns the number of nodes in the graph.

neighbors(G, n)

Returns a list of nodes connected to node n.

all_neighbors(graph, node)

Returns all of the neighbors of a node in the graph.

non_neighbors(graph, node)

Returns the non-neighbors of the node in the graph.

common_neighbors(G, u, v)

Returns the common neighbors of two nodes in a graph.

Edges#

edges(G[, nbunch])

Returns an edge view of edges incident to nodes in nbunch.

number_of_edges(G)

Returns the number of edges in the graph.

density(G)

Returns the density of a graph.

non_edges(graph)

Returns the non-existent edges in the graph.

Self loops#

selfloop_edges(G[, data, keys, default])

Returns an iterator over selfloop edges.

number_of_selfloops(G)

Returns the number of selfloop edges.

nodes_with_selfloops(G)

Returns an iterator over nodes with self loops.

Attributes#

is_weighted(G[, edge, weight])

Returns True if G has weighted edges.

is_negatively_weighted(G[, edge, weight])

Returns True if G has negatively weighted edges.

Freezing graph structure#

freeze(G)

Modify graph to prevent further change by adding or removing nodes or edges.

is_frozen(G)

Returns True if graph is frozen.