update, delete

Author: BaseMax

test10.py

@@ -1,9 +1,16 @@
+import random
 import networkx as nx
 import matplotlib.pyplot as plt
 
-# Create a Barabasi-Albert graph with 10,000 nodes and m=3
-G = nx.barabasi_albert_graph(10000, 3)
+# Create a graph
+G = nx.Graph()
+G.add_nodes_from(range(10))
+G.add_edges_from([(0,1), (1,2), (2,3), (3,4), (4,5), (5,6), (6,7), (7,8), (8,9), (9,0)])
 
-# Draw a subgraph of the first 100 nodes using NetworkX and Matplotlib
-nx.draw(G.subgraph(range(100)), with_labels=False)
+# Set random colors for nodes and edges
+node_colors = [random.choice(['red', 'blue', 'green']) for _ in range(len(G.nodes()))]
+edge_colors = [random.choice(['red', 'blue', 'green']) for _ in range(len(G.edges()))]
+
+# Draw the graph with node and edge colors
+nx.draw(G, with_labels=True, node_color=node_colors, edge_color=edge_colors)
 plt.show()

test11.py

@@ -1,16 +1,33 @@
-import random
 import networkx as nx
 import matplotlib.pyplot as plt
+import numpy as np
 
-# Create a graph
-G = nx.Graph()
-G.add_nodes_from(range(10))
-G.add_edges_from([(0,1), (1,2), (2,3), (3,4), (4,5), (5,6), (6,7), (7,8), (8,9), (9,0)])
+# Create a random graph with 10,000 nodes and 50,000 edges
+G = nx.gnm_random_graph(1000, 5000)
 
-# Set random colors for nodes and edges
-node_colors = [random.choice(['red', 'blue', 'green']) for _ in range(len(G.nodes()))]
-edge_colors = [random.choice(['red', 'blue', 'green']) for _ in range(len(G.edges()))]
+# Set random weights for the edges
+weights = np.random.rand(len(G.edges))
+nx.set_edge_attributes(G, dict(zip(G.edges, weights)), 'weight')
 
-# Draw the graph with node and edge colors
-nx.draw(G, with_labels=True, node_color=node_colors, edge_color=edge_colors)
+# Generate a layout using the spring layout algorithm
+pos = nx.spring_layout(G)
+
+# Assign colors to the nodes based on their degree
+node_colors = np.array(list(dict(G.degree()).values()))
+node_colors = (node_colors - node_colors.min()) / (node_colors.max() - node_colors.min())
+
+# Assign colors to the edges based on their weight
+edge_weights = nx.get_edge_attributes(G, 'weight')
+edge_weights_norm = (np.array(list(edge_weights.values())) - min(edge_weights.values())) / (max(edge_weights.values()) - min(edge_weights.values()))
+cmap = plt.cm.get_cmap('Blues')
+edge_colors = cmap(edge_weights_norm)
+
+# Draw the graph using the layout and color scheme
+nx.draw(G, pos=pos, node_color=node_colors, edge_color=edge_colors, with_labels=False, node_size=5, width=0.5)
+
+# Add a title and subtitle to the plot
+plt.title("Random Graph with 10,000 Nodes and 50,000 Edges")
+plt.suptitle("Generated using the gnm_random_graph() function from NetworkX")
+
+# Display the plot
 plt.show()

test12.py

@@ -1,33 +1,17 @@
 import networkx as nx
 import matplotlib.pyplot as plt
-import numpy as np
 
-# Create a random graph with 10,000 nodes and 50,000 edges
-G = nx.gnm_random_graph(1000, 5000)
-
-# Set random weights for the edges
-weights = np.random.rand(len(G.edges))
-nx.set_edge_attributes(G, dict(zip(G.edges, weights)), 'weight')
+# Create a random graph with 50 nodes
+G = nx.fast_gnp_random_graph(50, 0.2)
 
 # Generate a layout using the spring layout algorithm
 pos = nx.spring_layout(G)
 
-# Assign colors to the nodes based on their degree
-node_colors = np.array(list(dict(G.degree()).values()))
-node_colors = (node_colors - node_colors.min()) / (node_colors.max() - node_colors.min())
-
-# Assign colors to the edges based on their weight
-edge_weights = nx.get_edge_attributes(G, 'weight')
-edge_weights_norm = (np.array(list(edge_weights.values())) - min(edge_weights.values())) / (max(edge_weights.values()) - min(edge_weights.values()))
-cmap = plt.cm.get_cmap('Blues')
-edge_colors = cmap(edge_weights_norm)
-
-# Draw the graph using the layout and color scheme
-nx.draw(G, pos=pos, node_color=node_colors, edge_color=edge_colors, with_labels=False, node_size=5, width=0.5)
+# Draw the graph using the layout and default settings
+nx.draw(G, pos)
 
-# Add a title and subtitle to the plot
-plt.title("Random Graph with 10,000 Nodes and 50,000 Edges")
-plt.suptitle("Generated using the gnm_random_graph() function from NetworkX")
+# Add a title to the plot
+plt.suptitle("Generated using the fast_gnp_random_graph() function from NetworkX", fontsize=12)
 
 # Display the plot
 plt.show()