top of page
Search

# Networkx Analysis In Machine Learning | Python Machine Learning Assignment Help | Codersarts

Before starting the networkx first, we know what is a graph? and why we use graphs?

In mathematics we will learn the graph and their applications like that:

It denoted by edge and vertices:

V = {A, B, C, D, F}

E = {((A,B), (B,C), etc}

Now we can say:

"Graphs are mathematical structures used to study pairwise relationships between objects and entities."

In data Science, it created using a package called "networkx" that makes it easy to draw the graphs.

### Graphs in python

We will be using the networkx package in Python.

It can install using the pip command.

Now we will creating simple graph uisng:

Step 1: In first step import networkx libraries

`import networkx as nx`

Step 2: Creating Graph

`G = nx.Graph() `

```# Add a node

```# Add edges

### Other Useful methods which is used to create graphs

```subgraph(G, nbunch)      - induced subgraph view of G on nodes in nbunch
union(G1,G2)             - graph union
disjoint_union(G1,G2)    - graph union assuming all nodes are different
cartesian_product(G1,G2) - return Cartesian product graph
compose(G1,G2)           - combine graphs identifying nodes common to both
complement(G)            - graph complement
create_empty_copy(G)     - return an empty copy of the same graph class
convert_to_undirected(G) - return an undirected representation of G
convert_to_directed(G)   - return a directed representation of G```

### Accessing edges and nodes

Nodes and Edges can be accessed together using the G.nodes() and G.edges()

`G.nodes()`

Output:

NodeView((1, 2, 3))

`G.edges()`

Output:

EdgeView([(1, 2), (1, 3), (2, 3)])

### Graph Visualization

Networkx provides basic functionality for visualizing graphs. matplotlib offers some convenience functions.

"GraphViz" is probably the best tool for us as it offers a Python interface in the form of "PyGrapgViz"

```%matplotlib inline
import matplotlib.pyplot as plt
nx.draw(G)```

Now working with graphViz, which is Install from Graphviz from the website:

```import pygraphviz as pgv
d={'1': {'2': None}, '2': {'1': None, '3': None}, '3': {'1': None}}
A = pgv.AGraph(data=d)
print(A) # This is the 'string' or simple representation of the Graph
```

Output:

strict graph "" { 1 -- 2; 2 -- 3; 3 -- 1; }

Need help Using realtime dataset you can contact us at below contact details: