Some Networkx Examples

Source

The Departure

This is a look at different ways of creating and manipulating graphs using NetworkX.

Imports

Python

from functools import partial
from pathlib import Path
import os

PyPi

from bokeh.models import HoverTool
import holoviews
import hvplot.pandas
import networkx
import numpy
import pandas

My Stuff

from graeae.visualization import EmbedHoloview

Set Up

SLUG = "some-networkx-examples"
OUTPUT_PATH = Path("../../files/posts/networks/" + SLUG)
Embed = partial(EmbedHoloview, folder_path=OUTPUT_PATH)

The Initiation

Different Ways to Create Network Graphs

NetworkX has a few different ways to create graphs, some more flexible than others. This is a non-exhaustive showing of some of them.

Adding Edges To An Existing Graph

You can create a graph using the graph constructors. This is an example of an undirected Graph.

graph_1 = networkx.Graph()

G1.add_edges_from([(0, 1),
                   (0, 2),
                   (0, 3),
                   (0, 5),
                   (1, 3),
                   (1, 6),
                   (3, 4),
                   (4, 5),
                   (4, 7),
                   (5, 8),
                   (8, 9)])

networkx.draw_networkx(G1)

nil

Adjacency List

with open("G_adjlist.txt") as reader:
    print(reader.read())

G2 = networkx.read_adjlist('G_adjlist.txt', nodetype=int)
G2.edges()

Adjacency Matrix

G_mat = numpy.array([[0, 1, 1, 1, 0, 1, 0, 0, 0, 0],
                     [1, 0, 0, 1, 0, 0, 1, 0, 0, 0],
                     [1, 0, 0, 0, 0, 0, 0, 0, 0, 0],
                     [1, 1, 0, 0, 1, 0, 0, 0, 0, 0],
                     [0, 0, 0, 1, 0, 1, 0, 1, 0, 0],
                     [1, 0, 0, 0, 1, 0, 0, 0, 1, 0],
                     [0, 1, 0, 0, 0, 0, 0, 0, 0, 0],
                     [0, 0, 0, 0, 1, 0, 0, 0, 0, 0],
                     [0, 0, 0, 0, 0, 1, 0, 0, 0, 1],
                     [0, 0, 0, 0, 0, 0, 0, 0, 1, 0]])
G_mat

G3 = networkx.Graph(G_mat)
G3.edges()

Edgelist

with open('G_edgelist.txt') as reader:
    print(reader.read())

G4 = networkx.read_edgelist('G_edgelist.txt', data=[('Weight', int)])

G4.edges(data=True)

Pandas DataFrame

G_df = pandas.read_csv('G_edgelist.txt', delim_whitespace=True, 
                       header=None, names=['n1', 'n2', 'weight'])
G_df

G5 = networkx.from_pandas_dataframe(G_df, 'n1', 'n2', edge_attr='weight')
G5.edges(data=True)

A Chess Example

with open('chess_graph.txt') as reader:
    count = 0
    for line in reader:
        print(line.strip())
        count += 1
        if count == 5:
            break

chess = networkx.read_edgelist('chess_graph.txt', data=[('outcome', int), ('timestamp', float)], 
                         create_using=networkx.MultiDiGraph())

chess.is_directed(), chess.is_multigraph()

chess.edges(data=True)[:5]

games_played = chess.degree()
games_played

max_value = max(games_played.values())
max_key, = [i for i in games_played.keys() if games_played[i] == max_value]

print('player {}\n{} games'.format(max_key, max_value))

df = pd.DataFrame(chess.edges(data=True), columns=['white', 'black', 'outcome'])
df.head()

df['outcome'] = df['outcome'].map(lambda x: x['outcome'])
df.head()

won_as_white = df[df['outcome']==1].groupby('white').sum()
won_as_black = df[df['outcome']==-1].groupby('black').sum()
win_count = won_as_white.add(won_as_black, fill_value=0)
print(win_count.head())

win_count.nlargest(5, 'outcome')

The Return