Prerequisite : Create an undirected Graph.

The basic Graph operations are as follows:

**Getting Subgraph from a Graph:**

Given a Graph, and a subset of its set of nodes, we can create a Subgraph by selecting these nodes and all the edges between them as were present in the original Graph. The following code will clearly illustrate this operation.

`import` `networkx as nx ` `import` `matplotlib.pyplot as plt ` ` ` `G ` `=` `nx.Graph() ` ` ` `plt.figure(figsize ` `=` `(` `9` `, ` `12` `)) ` `G.add_edges_from([(` `1` `, ` `2` `), (` `1` `, ` `3` `), (` `2` `, ` `3` `), (` `2` `, ` `4` `), (` `2` `, ` `5` `), (` `3` `, ` `4` `), ` ` ` `(` `4` `, ` `5` `), (` `4` `, ` `6` `), (` `5` `, ` `7` `), (` `5` `, ` `8` `), (` `7` `, ` `8` `)]) ` ` ` `# original Graph created ` `plt.subplot(` `211` `) ` `print` `(` `"The original Graph:"` `) ` ` ` `nx.draw_networkx(G) ` |

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The original Graph:

`H ` `=` `G.subgraph([` `1` `, ` `2` `, ` `3` `, ` `4` `]) ` `# [1, 2, 3, 4] is the subset of ` `# the original set of nodes ` ` ` `plt.subplot(` `212` `) ` `print` `(` `"The Subgraph:"` `) ` `nx.draw_networkx(H) ` |

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The Subgraph:

The original Graph G has nodes from 1 to 8. We have selected nodes 1, 2, 3 and 4 and created a Subgraph H which has 5 edges which were present among them in the original graph G.

**Union of two Graphs:**

Given two graphs G and H, the union of the 2 graphs create a single Graph which may have multiple connected components. But we have to keep in mind that the set of nodes of G and H should be disjoint, in other words, the two graphs shouldn’t have any nodes in common.

`import` `networkx as nx ` `import` `matplotlib.pyplot as plt ` ` ` `G ` `=` `nx.Graph() ` ` ` `plt.figure(figsize ` `=` `(` `9` `, ` `12` `)) ` `G.add_edges_from([(` `1` `, ` `2` `), (` `1` `, ` `3` `), (` `2` `, ` `3` `), (` `2` `, ` `4` `), (` `2` `, ` `5` `), (` `3` `, ` `4` `), ` ` ` `(` `4` `, ` `5` `), (` `4` `, ` `6` `), (` `5` `, ` `7` `), (` `5` `, ` `8` `), (` `7` `, ` `8` `)]) ` ` ` `# First Graph created ` `plt.subplot(` `311` `) ` `nx.draw_networkx(G) ` ` ` `H ` `=` `nx.Graph() ` `H.add_edges_from([(` `13` `, ` `14` `), (` `13` `, ` `15` `), (` `13` `, ` `9` `), ` ` ` `(` `14` `, ` `15` `), (` `15` `, ` `10` `), (` `9` `, ` `10` `)]) ` ` ` `# Second Graph created ` `plt.subplot(` `312` `) ` `nx.draw_networkx(H) ` ` ` ` ` `I ` `=` `nx.union(G, H) ` `plt.subplot(` `313` `) ` `nx.draw_networkx(I) ` |

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The newly formed graph I is the union of graphs g and H. If we do have common nodes between two graphs and still want to get their union then we will use another function called `disjoint_set()`

I = nx.disjoint_set(G, H)

This will rename the common nodes and form a similar Graph.

**Cartesian Product of two Graphs:**

Given two graphs, G and H the cartesian product creates a new Graph, I = G*H. The set of nodes of I is the cartesian product of sets of nodes of G and H that is, V(I) = V(G)*V(H).

An edge ((), ()) exists if and only if:

- i=k and exists as an edge in H
- j=l and exists as an edge in G

`import` `networkx as nx ` `import` `matplotlib.pyplot as plt ` ` ` `G ` `=` `nx.Graph() ` ` ` `plt.figure(figsize ` `=` `(` `9` `, ` `18` `)) ` `G.add_edges_from([(` `1` `, ` `2` `), (` `2` `, ` `3` `)]) ` ` ` `# First Graph created ` `plt.subplot(` `311` `) ` `nx.draw_networkx(G) ` ` ` `H ` `=` `nx.Graph() ` `H.add_edges_from([(` `6` `, ` `7` `)]) ` `# Second Graph created ` `plt.subplot(` `312` `) ` `nx.draw_networkx(H) ` ` ` ` ` `I ` `=` `nx.cartesian_product(G, H) ` `plt.subplot(` `313` `) ` `nx.draw_networkx(I) ` |

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This representation clearly shows how the product of the first 2 graphs result in the third graph.

**Composition of two graphs: **

Given two graphs G and H, if they have no common nodes then the composition of the two of them will result in a single Graph with 2 connected components (assuming G and H are connected graphs). This is the same result that we will obtain if we use `nx.union(G, H)`

or `nx.disjoint_union(G, H)`

.

But if G and H have common nodes the resultant composition of these two graphs will result in a single connected Graph in such a way that G and H are subgraphs of the new Graph.

`import` `networkx as nx ` `import` `matplotlib.pyplot as plt ` ` ` `G ` `=` `nx.Graph() ` ` ` `plt.figure(figsize ` `=` `(` `9` `, ` `15` `)) ` `G.add_edges_from([(` `1` `, ` `2` `), (` `1` `, ` `3` `), (` `2` `, ` `3` `), (` `2` `, ` `4` `)]) ` ` ` `# First Graph created ` `plt.subplot(` `311` `) ` `nx.draw_networkx(G) ` ` ` `H ` `=` `nx.Graph() ` `H.add_edges_from([(` `3` `, ` `7` `), (` `7` `, ` `4` `), (` `3` `, ` `4` `)]) ` `# Second Graph created ` `plt.subplot(` `312` `) ` `nx.draw_networkx(H) ` ` ` ` ` `I ` `=` `nx.compose(G, H) ` `plt.subplot(` `313` `) ` `nx.draw_networkx(I) ` |

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The diagrams clearly visualize how the first two graphs compose together to form the third Graph.

**Complement of a Graph:**

Given a graph G, the complement of G (say, H) has all the nodes of G. It has all the possible edges that G does not have. Let V and E be the set of nodes and edges of G, then H has `{(|V|*(|V|-1))/2 - |E|} `

number of edges. Thus the complement of a complete Graph will have no edges.

`import` `networkx as nx ` `import` `matplotlib.pyplot as plt ` ` ` `G ` `=` `nx.Graph() ` ` ` `plt.figure(figsize ` `=` `(` `9` `, ` `16` `)) ` `G.add_edges_from([(` `1` `, ` `2` `), (` `1` `, ` `3` `), (` `2` `, ` `3` `), (` `2` `, ` `4` `)]) ` ` ` `# Original Graph created ` `plt.subplot(` `211` `) ` `nx.draw_networkx(G) ` ` ` `H ` `=` `nx.complement(G) ` `plt.subplot(` `212` `) ` `nx.draw_networkx(H) ` |

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**Convert to Directed:**

Given an undirected Graph G, this Networkx function will convert it to a Directed Graph by replacing its edges with 2-way Directed edges.

`import` `networkx as nx ` `import` `matplotlib.pyplot as plt ` ` ` `G ` `=` `nx.Graph() ` ` ` `plt.figure(figsize ` `=` `(` `9` `, ` `16` `)) ` `G.add_edges_from([(` `1` `, ` `2` `), (` `1` `, ` `3` `), (` `2` `, ` `3` `), (` `2` `, ` `4` `)]) ` `# Original Undirected Graph created ` ` ` `plt.subplot(` `211` `) ` `nx.draw_networkx(G) ` ` ` `H ` `=` `nx.to_directed(G) ` `plt.subplot(` `212` `) ` `nx.draw_networkx(H) ` |

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**Convert to Undirected:**

Given a Directed Graph G, this Networkx function will convert it to an Undirected graph by converting all its directed edges to undirected edges. If two edges exist between a pair of nodes with different attributes (weights, colour etc.), then only one edge is created with an arbitrary choice of which edge data to use.

`import` `networkx as nx ` `import` `matplotlib.pyplot as plt ` ` ` `G ` `=` `nx.DiGraph() ` ` ` `plt.figure(figsize ` `=` `(` `9` `, ` `16` `)) ` `G.add_edges_from([(` `1` `, ` `2` `), (` `1` `, ` `3` `), (` `2` `, ` `4` `)]) ` ` ` `# Original Directed Graph created ` `plt.subplot(` `211` `) ` `nx.draw_networkx(G) ` ` ` `H ` `=` `nx.to_undirected(G) ` `plt.subplot(` `212` `) ` `nx.draw_networkx(H) ` |

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Now, we will discuss the various Special Graphs offered by Networkx module.

**Petersen Graph:** The Petersen graph is an undirected graph with 10 vertices and 15 edges. It is a small graph that serves as a useful example and counterexample for many problems in graph theory.

`import` `networkx as nx ` `import` `matplotlib.pyplot as plt ` ` ` `plt.figure(figsize ` `=` `(` `9` `, ` `100` `)) ` ` ` `# Peterson Graph ` `plt.subplot(` `12` `, ` `1` `, ` `1` `) ` `G ` `=` `nx.petersen_graph() ` `nx.draw_networkx(G) ` |

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**Tutte Graph: ** The Tutte graph is a 3-regular graph with 46 vertices and 69 edges. The Tutte graph is a cubic polyhedral graph, but is non-hamiltonian. Therefore, it is a counterexample to Tait’s conjecture. that every 3-regular polyhedron has a Hamiltonian cycle.

`# Tutte Graph ` `plt.subplot(` `12` `, ` `1` `, ` `2` `) ` `G ` `=` `nx.tutte_graph() ` `nx.draw_networkx(G) ` |

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**Sedgewick Maze Graph:** Sedgewick’s Maze Algorithm is used to generate large mazes. Networkx function pertaining to this method returns a small maze with a cycle.

`# Sedgewick Maze Graph ` `plt.subplot(` `12` `, ` `1` `, ` `3` `) ` `G ` `=` `nx.sedgewick_maze_graph() ` `nx.draw_networkx(G) ` |

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**Tetrahedral Graph:** This returns a complete Graph with four nodes which are in the shape of a tetrahedron.

`# Tetrahedral Graph ` `plt.subplot(` `12` `, ` `1` `, ` `4` `) ` `G ` `=` `nx.tetrahedral_graph() ` `nx.draw_networkx(G) ` |

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**Complete Graph:** Returns a Complete Graph with given number of edges.

`# Complete Graph with 5 nodes ` `plt.subplot(` `12` `, ` `1` `, ` `5` `) ` `G ` `=` `nx.complete_graph(` `6` `) ` `nx.draw_networkx(G) ` |

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**Complete Bipartite Graph:** Given two numbers n and m, it returns a Graph with two sets of n and m nodes in such a way that a node of one set is connected to all the nodes of the other set but with no node of its own set. This type of Graph is known as Bipartite.

`# Complete Bipartite Graph with 5 and 3 nodes ` `plt.subplot(` `12` `, ` `1` `, ` `6` `) ` `G ` `=` `nx.complete_bipartite_graph(` `5` `, ` `3` `) ` `nx.draw_networkx(G) ` |

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**Barbell Graph:** Given two parameters n and m, it returns a Graph with two cliques of n nodes which are connected via m nodes in between.

`# Barbell Graph with clique of 4 and bridging of 2 nodes ` `plt.subplot(` `12` `, ` `1` `, ` `7` `) ` ` ` `G ` `=` `nx.barbell_graph(` `4` `, ` `2` `) ` `nx.draw_networkx(G) ` |

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**Lollipop Graph:** Given two parameters n and m, it returns a Graph with a clique of n vertices connected to a path of m nodes.

`# Lollipop Graph with clique of 5 and path of 2 nodes ` `plt.subplot(` `12` `, ` `1` `, ` `8` `) ` `G ` `=` `nx.lollipop_graph(` `5` `, ` `2` `) ` `nx.draw_networkx(G) ` |

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**Erdos Renyi Graph:** Given two parameters n and p, it returns a Graph with n nodes with a probability of p for selecting each edge. For further details, see this article.

`# Erdos Renyi Graph with 20 nodes and probability of 0.25 ` `plt.subplot(` `12` `, ` `1` `, ` `9` `) ` ` ` `G ` `=` `nx.erdos_renyi_graph(` `20` `, ` `0.25` `) ` `nx.draw_networkx(G) ` |

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**Watts Strogatz Graph:** Given three parameters n, k and p, returns a Small World Network of n nodes each of which is connected to k nearest neighbors with a probability of p of rewiring each edge.

`# Watts Strogatz Graph with 20 nodes, ` `# 4 neighbours and probability of 0.2 ` `plt.subplot(` `12` `, ` `1` `, ` `10` `) ` `G ` `=` `nx.watts_strogatz_graph(` `20` `, ` `4` `, ` `0.2` `) ` `nx.draw_networkx(G) ` |

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**Barabasi Albert Graph:** Given two parameters n and m, returns a Barabasi Albert preferential attachment graph with n nodes and m number of edges to attach from a new node to existing nodes.

`# Barabasi Albert Graph with 20 nodes and 3 attaching nodes ` `plt.subplot(` `12` `, ` `1` `, ` `11` `) ` `G ` `=` `nx.barabasi_albert_graph(` `20` `, ` `3` `) ` `nx.draw_networkx(G) ` |

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**Random Lobster Graph:** Given three parameters n, p1 and p2, returns a lobster Graph. A lobster is a tree that reduces to a caterpillar when pruning all leaf nodes. A caterpillar is a tree that reduces to a path graph when pruning all leaf nodes. Graph has n nodes in backbone, p1 probability of adding an edge to the backbone, p2 probability of adding an edge a level beyond backbone.

`# Random Lobster Graph with 30 base ` `# nodes and probabilites of 0.8 ` `plt.subplot(` `12` `, ` `1` `, ` `12` `) ` ` ` `G ` `=` `nx.random_lobster(` `30` `, ` `0.8` `, ` `0.8` `) ` `nx.draw_networkx(G) ` |

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