Functions¶

In [1]:

import matplotlib.pyplot as plt
import networkx as nx
import seaborn as sns
sns.set()
%matplotlib inline

In [2]:

import warnings
import matplotlib.cbook
warnings.filterwarnings("ignore",category=matplotlib.cbook.mplDeprecation)

In [3]:

G = nx.karate_club_graph()
nx.draw(G)

1. 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]) Print short summary of information for the graph G or the 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 SubGraph view of G showing only nodes in nbunch.
restricted_view(G, nodes, edges) Returns a view of G with hidden nodes and edges.
reverse_view(digraph) Provide a reverse view of the digraph with edges reversed.
edge_subgraph(G, edges) Returns a view of the subgraph induced by the specified edges.

In [4]:

nx.degree(G)

Out[4]:

DegreeView({0: 16, 1: 9, 2: 10, 3: 6, 4: 3, 5: 4, 6: 4, 7: 4, 8: 5, 9: 2, 10: 3, 11: 1, 12: 2, 13: 5, 14: 2, 15: 2, 16: 2, 17: 2, 18: 2, 19: 3, 20: 2, 21: 2, 22: 2, 23: 5, 24: 3, 25: 3, 26: 2, 27: 4, 28: 3, 29: 4, 30: 4, 31: 6, 32: 12, 33: 17})

In [5]:

nx.degree_histogram(G)

Out[5]:

[0, 1, 11, 6, 6, 3, 2, 0, 0, 1, 1, 0, 1, 0, 0, 0, 1, 1]

In [6]:

nx.density(G)

Out[6]:

0.13903743315508021

In [7]:

nx.info(G)

Out[7]:

"Name: Zachary's Karate Club\nType: Graph\nNumber of nodes: 34\nNumber of edges: 78\nAverage degree:   4.5882"

In [19]:

DG = nx.to_directed(G)
nx.draw(DG)

In [20]:

nx.is_directed(G)

Out[20]:

False

In [21]:

nx.is_directed(DG)

Out[21]:

True

2. Node Functions¶

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.

In [39]:

nx.nodes(G),G.nodes()

Out[39]:

(NodeView((0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33)),
 NodeView((0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33)))

In [32]:

nx.number_of_nodes(G), G.number_of_nodes()

Out[32]:

(34, 34)

In [33]:

[n for n in nx.neighbors(G,1)],[n for n in G.neighbors(1)]

Out[33]:

([0, 2, 3, 7, 13, 17, 19, 21, 30], [0, 2, 3, 7, 13, 17, 19, 21, 30])

In [38]:

for node in G.nodes():
    print(node,"|",[n for n in G.neighbors(node)])

0 | [1, 2, 3, 4, 5, 6, 7, 8, 10, 11, 12, 13, 17, 19, 21, 31]
1 | [0, 2, 3, 7, 13, 17, 19, 21, 30]
2 | [0, 1, 3, 7, 8, 9, 13, 27, 28, 32]
3 | [0, 1, 2, 7, 12, 13]
4 | [0, 6, 10]
5 | [0, 6, 10, 16]
6 | [0, 4, 5, 16]
7 | [0, 1, 2, 3]
8 | [0, 2, 30, 32, 33]
9 | [2, 33]
10 | [0, 4, 5]
11 | [0]
12 | [0, 3]
13 | [0, 1, 2, 3, 33]
14 | [32, 33]
15 | [32, 33]
16 | [5, 6]
17 | [0, 1]
18 | [32, 33]
19 | [0, 1, 33]
20 | [32, 33]
21 | [0, 1]
22 | [32, 33]
23 | [25, 27, 29, 32, 33]
24 | [25, 27, 31]
25 | [23, 24, 31]
26 | [29, 33]
27 | [2, 23, 24, 33]
28 | [2, 31, 33]
29 | [23, 26, 32, 33]
30 | [1, 8, 32, 33]
31 | [0, 24, 25, 28, 32, 33]
32 | [2, 8, 14, 15, 18, 20, 22, 23, 29, 30, 31, 33]
33 | [8, 9, 13, 14, 15, 18, 19, 20, 22, 23, 26, 27, 28, 29, 30, 31, 32]

In [43]:

[n for n in nx.all_neighbors(G,33)]

Out[43]:

[8, 9, 13, 14, 15, 18, 19, 20, 22, 23, 26, 27, 28, 29, 30, 31, 32]

In [46]:

[n for n in nx.common_neighbors(G, 32, 33)]

Out[46]:

[8, 14, 15, 18, 20, 22, 23, 29, 30, 31]

Edge Functions¶

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.

In [47]:

nx.edges(G), G.edges()

Out[47]:

(EdgeView([(0, 1), (0, 2), (0, 3), (0, 4), (0, 5), (0, 6), (0, 7), (0, 8), (0, 10), (0, 11), (0, 12), (0, 13), (0, 17), (0, 19), (0, 21), (0, 31), (1, 2), (1, 3), (1, 7), (1, 13), (1, 17), (1, 19), (1, 21), (1, 30), (2, 3), (2, 7), (2, 8), (2, 9), (2, 13), (2, 27), (2, 28), (2, 32), (3, 7), (3, 12), (3, 13), (4, 6), (4, 10), (5, 6), (5, 10), (5, 16), (6, 16), (8, 30), (8, 32), (8, 33), (9, 33), (13, 33), (14, 32), (14, 33), (15, 32), (15, 33), (18, 32), (18, 33), (19, 33), (20, 32), (20, 33), (22, 32), (22, 33), (23, 25), (23, 27), (23, 29), (23, 32), (23, 33), (24, 25), (24, 27), (24, 31), (25, 31), (26, 29), (26, 33), (27, 33), (28, 31), (28, 33), (29, 32), (29, 33), (30, 32), (30, 33), (31, 32), (31, 33), (32, 33)]),
 EdgeView([(0, 1), (0, 2), (0, 3), (0, 4), (0, 5), (0, 6), (0, 7), (0, 8), (0, 10), (0, 11), (0, 12), (0, 13), (0, 17), (0, 19), (0, 21), (0, 31), (1, 2), (1, 3), (1, 7), (1, 13), (1, 17), (1, 19), (1, 21), (1, 30), (2, 3), (2, 7), (2, 8), (2, 9), (2, 13), (2, 27), (2, 28), (2, 32), (3, 7), (3, 12), (3, 13), (4, 6), (4, 10), (5, 6), (5, 10), (5, 16), (6, 16), (8, 30), (8, 32), (8, 33), (9, 33), (13, 33), (14, 32), (14, 33), (15, 32), (15, 33), (18, 32), (18, 33), (19, 33), (20, 32), (20, 33), (22, 32), (22, 33), (23, 25), (23, 27), (23, 29), (23, 32), (23, 33), (24, 25), (24, 27), (24, 31), (25, 31), (26, 29), (26, 33), (27, 33), (28, 31), (28, 33), (29, 32), (29, 33), (30, 32), (30, 33), (31, 32), (31, 33), (32, 33)]))

In [53]:

nx.edges(G, 33)

Out[53]:

EdgeDataView([(33, 8), (33, 9), (33, 13), (33, 14), (33, 15), (33, 18), (33, 19), (33, 20), (33, 22), (33, 23), (33, 26), (33, 27), (33, 28), (33, 29), (33, 30), (33, 31), (33, 32)])

In [55]:

for node in G.nodes():
    print(node,"|",nx.edges(G,node))

0 | [(0, 1), (0, 2), (0, 3), (0, 4), (0, 5), (0, 6), (0, 7), (0, 8), (0, 10), (0, 11), (0, 12), (0, 13), (0, 17), (0, 19), (0, 21), (0, 31)]
1 | [(1, 0), (1, 2), (1, 3), (1, 7), (1, 13), (1, 17), (1, 19), (1, 21), (1, 30)]
2 | [(2, 0), (2, 1), (2, 3), (2, 7), (2, 8), (2, 9), (2, 13), (2, 27), (2, 28), (2, 32)]
3 | [(3, 0), (3, 1), (3, 2), (3, 7), (3, 12), (3, 13)]
4 | [(4, 0), (4, 6), (4, 10)]
5 | [(5, 0), (5, 6), (5, 10), (5, 16)]
6 | [(6, 0), (6, 4), (6, 5), (6, 16)]
7 | [(7, 0), (7, 1), (7, 2), (7, 3)]
8 | [(8, 0), (8, 2), (8, 30), (8, 32), (8, 33)]
9 | [(9, 2), (9, 33)]
10 | [(10, 0), (10, 4), (10, 5)]
11 | [(11, 0)]
12 | [(12, 0), (12, 3)]
13 | [(13, 0), (13, 1), (13, 2), (13, 3), (13, 33)]
14 | [(14, 32), (14, 33)]
15 | [(15, 32), (15, 33)]
16 | [(16, 5), (16, 6)]
17 | [(17, 0), (17, 1)]
18 | [(18, 32), (18, 33)]
19 | [(19, 0), (19, 1), (19, 33)]
20 | [(20, 32), (20, 33)]
21 | [(21, 0), (21, 1)]
22 | [(22, 32), (22, 33)]
23 | [(23, 25), (23, 27), (23, 29), (23, 32), (23, 33)]
24 | [(24, 25), (24, 27), (24, 31)]
25 | [(25, 23), (25, 24), (25, 31)]
26 | [(26, 29), (26, 33)]
27 | [(27, 2), (27, 23), (27, 24), (27, 33)]
28 | [(28, 2), (28, 31), (28, 33)]
29 | [(29, 23), (29, 26), (29, 32), (29, 33)]
30 | [(30, 1), (30, 8), (30, 32), (30, 33)]
31 | [(31, 0), (31, 24), (31, 25), (31, 28), (31, 32), (31, 33)]
32 | [(32, 2), (32, 8), (32, 14), (32, 15), (32, 18), (32, 20), (32, 22), (32, 23), (32, 29), (32, 30), (32, 31), (32, 33)]
33 | [(33, 8), (33, 9), (33, 13), (33, 14), (33, 15), (33, 18), (33, 19), (33, 20), (33, 22), (33, 23), (33, 26), (33, 27), (33, 28), (33, 29), (33, 30), (33, 31), (33, 32)]

In [49]:

nx.number_of_edges(G),G.number_of_edges()

Out[49]:

(78, 78)

In [52]:

nx.density(G)

Out[52]:

0.13903743315508021

Graph 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.
set_node_attributes(G, values[, name]) Sets node attributes from a given value or dictionary of values.
get_node_attributes(G, name) Get node attributes from graph
set_edge_attributes(G, values[, name]) Sets edge attributes from a given value or dictionary of values.
get_edge_attributes(G, name) Get edge attributes from graph

In [56]:

nx.is_weighted(G)

Out[56]:

False

In [58]:

nx.is_negatively_weighted(G)

Out[58]:

False

In [61]:

nx.get_node_attributes(G, 33)

Out[61]:

{}

In [62]:

nx.get_edge_attributes(G,33)

Out[62]:

{}

In [ ]: