Network Analysis with NetworkX
import networkx as nx # Create an empty graph G = nx.Graph() # Display the graph print(G)
Graph with 0 nodes and 0 edges
import networkx as nx # Create an empty graph G = nx.Graph() # Add a single node G.add_node(1) # Add multiple nodes G.add_nodes_from([2, 3, 4]) # Display nodes print(G.nodes)
[1, 2, 3, 4]
import networkx as nx # Create an empty graph G = nx.Graph() # Add nodes G.add_nodes_from([1, 2, 3, 4]) # Add a single edge G.add_edge(1, 2) # Add multiple edges G.add_edges_from([(2, 3), (3, 4)]) # Display edges print(G.edges)
[(1, 2), (2, 3), (3, 4)]
import networkx as nx import matplotlib.pyplot as plt # Create a graph G = nx.Graph() G.add_edges_from([(1, 2), (2, 3), (3, 4), (4, 1)]) # Draw the graph nx.draw(G, with_labels=True, node_color='lightblue', font_weight='bold') plt.show()
import networkx as nx
# Create a graph
G = nx.Graph()
G.add_edges_from([(1, 2), (2, 3), (3, 4), (4, 1), (1, 3)])
# Compute degree for each node
degree_dict = dict(G.degree())
print("Degree of each node:", degree_dict)Degree of each node: {1: 3, 2: 2, 3: 3, 4: 2}
import networkx as nx
# Create a graph
G = nx.Graph()
G.add_edges_from([(1, 2), (2, 3), (3, 4), (4, 1), (1, 3)])
# Compute the shortest path between node 1 and node 4
shortest_path = nx.shortest_path(G, source=1, target=4)
print("Shortest path from node 1 to node 4:", shortest_path)Shortest path from node 1 to node 4: [1, 4]
import networkx as nx
# Create a graph
G = nx.Graph()
G.add_edges_from([(1, 2), (2, 3), (3, 4), (4, 1), (1, 3)])
# Compute clustering coefficient for each node
clustering_dict = nx.clustering(G)
print("Clustering coefficient of each node:", clustering_dict)Clustering coefficient of each node: {1: 0.6666666666666666, 2: 1.0, 3: 0.6666666666666666, 4: 1.0}
import networkx as nx
# Create a directed graph
G = nx.DiGraph()
# Add edges
edges = [(1, 2), (2, 3), (3, 1), (1, 4), (4, 1), (4, 3)]
G.add_edges_from(edges)
# Compute PageRank
pagerank = nx.pagerank(G)
print("PageRank:", pagerank)PageRank: {1: 0.35105903526129834, 2: 0.18669946835938991, 3: 0.2755420280199218, 4: 0.18669946835938991}
import networkx as nx
from networkx.algorithms.community import girvan_newman
import matplotlib.pyplot as plt
# Create a graph
G = nx.karate_club_graph()
# Draw the graph
nx.draw(G, with_labels=True, node_color='lightblue', font_weight='bold')
plt.show()
# Detect communities
communities = girvan_newman(G)
first_level_communities = next(communities)
community_list = sorted(map(sorted, first_level_communities))
print("Communities:", community_list)
Communities: [[0, 1, 3, 4, 5, 6, 7, 10, 11, 12, 13, 16, 17, 19, 21], [2, 8, 9, 14, 15, 18, 20, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33]]
import networkx as nx
import matplotlib.pyplot as plt
# Create a graph
G = nx.karate_club_graph()
# Draw the graph
nx.draw(G, with_labels=True, node_color='lightblue', font_weight='bold')
plt.show()
# Compute degree centrality
degree_centrality = nx.degree_centrality(G)
print("Degree Centrality:", degree_centrality)
Degree Centrality: {0: 0.48484848484848486, 1: 0.2727272727272727, 2: 0.30303030303030304, 3: 0.18181818181818182, 4: 0.09090909090909091, 5: 0.12121212121212122, 6: 0.12121212121212122, 7: 0.12121212121212122, 8: 0.15151515151515152, 9: 0.06060606060606061, 10: 0.09090909090909091, 11: 0.030303030303030304, 12: 0.06060606060606061, 13: 0.15151515151515152, 14: 0.06060606060606061, 15: 0.06060606060606061, 16: 0.06060606060606061, 17: 0.06060606060606061, 18: 0.06060606060606061, 19: 0.09090909090909091, 20: 0.06060606060606061, 21: 0.06060606060606061, 22: 0.06060606060606061, 23: 0.15151515151515152, 24: 0.09090909090909091, 25: 0.09090909090909091, 26: 0.06060606060606061, 27: 0.12121212121212122, 28: 0.09090909090909091, 29: 0.12121212121212122, 30: 0.12121212121212122, 31: 0.18181818181818182, 32: 0.36363636363636365, 33: 0.5151515151515151}
import networkx as nx
import matplotlib.pyplot as plt
# Create a graph
G = nx.karate_club_graph()
# Draw the graph
nx.draw(G, with_labels=True, node_color='lightblue', font_weight='bold')
plt.show()
# Compute betweenness centrality
betweenness_centrality = nx.betweenness_centrality(G)
print("Betweenness Centrality:", betweenness_centrality)
Betweenness Centrality: {0: 0.43763528138528146, 1: 0.053936688311688304, 2: 0.14365680615680618, 3: 0.011909271284271283, 4: 0.0006313131313131313, 5: 0.02998737373737374, 6: 0.029987373737373736, 7: 0.0, 8: 0.05592682780182781, 9: 0.0008477633477633478, 10: 0.0006313131313131313, 11: 0.0, 12: 0.0, 13: 0.04586339586339586, 14: 0.0, 15: 0.0, 16: 0.0, 17: 0.0, 18: 0.0, 19: 0.03247504810004811, 20: 0.0, 21: 0.0, 22: 0.0, 23: 0.017613636363636363, 24: 0.0022095959595959595, 25: 0.0038404882154882154, 26: 0.0, 27: 0.02233345358345358, 28: 0.0017947330447330447, 29: 0.0029220779220779218, 30: 0.014411976911976909, 31: 0.13827561327561325, 32: 0.145247113997114, 33: 0.30407497594997596}
import networkx as nx
import matplotlib.pyplot as plt
# Create a graph
G = nx.karate_club_graph()
# Draw the graph
nx.draw(G, with_labels=True, node_color='lightblue', font_weight='bold')
plt.show()
# Compute closeness centrality
closeness_centrality = nx.closeness_centrality(G)
print("Closeness Centrality:", closeness_centrality)
Closeness Centrality: {0: 0.5689655172413793, 1: 0.4852941176470588, 2: 0.559322033898305, 3: 0.4647887323943662, 4: 0.3793103448275862, 5: 0.38372093023255816, 6: 0.38372093023255816, 7: 0.44, 8: 0.515625, 9: 0.4342105263157895, 10: 0.3793103448275862, 11: 0.36666666666666664, 12: 0.3707865168539326, 13: 0.515625, 14: 0.3707865168539326, 15: 0.3707865168539326, 16: 0.28448275862068967, 17: 0.375, 18: 0.3707865168539326, 19: 0.5, 20: 0.3707865168539326, 21: 0.375, 22: 0.3707865168539326, 23: 0.39285714285714285, 24: 0.375, 25: 0.375, 26: 0.3626373626373626, 27: 0.4583333333333333, 28: 0.4520547945205479, 29: 0.38372093023255816, 30: 0.4583333333333333, 31: 0.5409836065573771, 32: 0.515625, 33: 0.55}
import networkx as nx
import matplotlib.pyplot as plt
# Create a graph
G = nx.karate_club_graph()
# Draw the graph
nx.draw(G, with_labels=True, node_color='lightblue', font_weight='bold')
plt.show()
# Perform BFS traversal from node 0
bfs_edges = list(nx.bfs_edges(G, source=0))
print("BFS Edges:", bfs_edges)
BFS Edges: [(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, 30), (2, 9), (2, 27), (2, 28), (2, 32), (5, 16), (8, 33), (31, 24), (31, 25), (27, 23), (32, 14), (32, 15), (32, 18), (32, 20), (32, 22), (32, 29), (33, 26)]
import networkx as nx
import matplotlib.pyplot as plt
# Create a graph
G = nx.karate_club_graph()
# Draw the graph
nx.draw(G, with_labels=True, node_color='lightblue', font_weight='bold')
plt.show()
# Perform DFS traversal from node 0
dfs_edges = list(nx.dfs_edges(G, source=0))
print("DFS Edges:", dfs_edges)
DFS Edges: [(0, 1), (1, 2), (2, 3), (3, 7), (3, 12), (3, 13), (13, 33), (33, 8), (8, 30), (30, 32), (32, 14), (32, 15), (32, 18), (32, 20), (32, 22), (32, 23), (23, 25), (25, 24), (24, 27), (24, 31), (31, 28), (23, 29), (29, 26), (33, 9), (33, 19), (1, 17), (1, 21), (0, 4), (4, 6), (6, 5), (5, 10), (5, 16), (0, 11)]
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