One of the powerful library used for graph building activities is NetworkX. It is widely used in solving graph problems and network related queries. Lets have a look into NetworkX now.

In order to use it with python import it,

import networkx as nx

The following basic graph types are provided as Python classes:

**Graph**

This class implements an undirected graph. It ignores multiple edges between two nodes. It does allow

self-loop edges between a node and itself.

**DiGraph**

Directed graphs, that is, graphs with directed edges. Provides operations common to directed graphs, (a

subclass of Graph).

**MultiGraph**

A flexible graph class that allows multiple undirected edges between pairs of nodes. The additional

flexibility leads to some degradation in performance, though usually not significant.

**MultiDiGraph**

A directed version of a MultiGraph

g=nx.Graph() g.add_edge(1,3,weight=0.2) g.add_edge(3,2,weight=.3) nx.draw(g)

we can even make edges or not as per requirement, like the code below:

import matplotlib.pyplot as plt g2=nx.Graph() g2.add_edge('A','B',weight=0.9) g2.add_node('C') #nx.draw(g2) pos = nx.spring_layout(g2) # compute graph layout nx.draw(g2, pos, node_size=700) # draw nodes and edges nx.draw_networkx_labels(g2, pos) labels = nx.get_edge_attributes(g2, 'weight') nx.draw_networkx_edge_labels(g2, pos, edge_labels=labels) plt.show(g2)

Now, if we go for a shortest path algorithm, networkx really rocks, lets take an example;

G=nx.Graph() e = [('a', 'b', 0.3), ('b', 'c', 0.9), ('a', 'c', 0.5), ('c', 'd', 1.2)] G.add_weighted_edges_from(e) pos = nx.spring_layout(G) nx.draw(G,pos,node_size=700) nx.draw_networkx_labels(G, pos) labels = nx.get_edge_attributes(G, 'weight') nx.draw_networkx_edge_labels(G, pos, edge_labels=labels) plt.show(G)

print(nx.dijkstra_path(G, 'a', 'd'))

['a', 'c', 'd']

We can even write few modular code to check the adjacency matrix and number of nodes relationship,

#Asignment 2 Comparision for i in range(3): GraphA=lstA[i] GraphB=lstB[i] Aad = nx.adjacency_matrix(GraphA) Bad = nx.adjacency_matrix(GraphB) count=i count=count+1 print('-------------****************-------------') print('Number of nodes: ',count*100) for k in range(2,4): print('An^k','n',count*100,'k',k) A_nk=np.linalg.matrix_power(Aad.todense(), k) #print(B_nk) plt.plot(A_nk) plt.show() print('Bn^k','n',count*100,'k',k) B_nk=np.linalg.matrix_power(Bad.todense(), k) #print(B_nk) plt.plot(B_nk) plt.show() print('------************------')

-------------****************------------- Number of nodes: 100 An^k n 100 k 2

Bn^k n 100 k 2

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