# Read the networkg = nx.read_pajek(“karate.paj”)# Get basic information about t

# Read the networkg = nx.read_pajek(“karate.paj”)# Get basic information about the networkinfo = nx.info(g)print(info)# Get the number of nodesnum_nodes = nx.number_of_nodes(g)print(num_nodes)# Get the number of edgesnum_edges = nx.number_of_edges(g)print(num_edges)# Check if the graph is directedis_directed = nx.is_directed(g)print(is_directed)
Similarly, we can analyze networks in GraphML format:
# Read the networkg = nx.read_graphml(“wikipedia.graphml”)# Get basic information about the networkinfo = nx.info(g)print(info)# Get the number of nodesnum_nodes = nx.number_of_nodes(g)print(num_nodes)# Get the number of edgesnum_edges = nx.number_of_edges(g)print(num_edges)# Check if the graph is directedis_directed = nx.is_directed(g)print(is_directed)Finally, let’s analyze the network in GEXF format:
# Read the networkg = nx.read_gexf(“network.gexf”)# Get basic information about the networkinfo = nx.info(g)print(info)# Get the number of nodesnum_nodes = nx.number_of_nodes(g)print(num_nodes)# Get the number of edgesnum_edges = nx.number_of_edges(g)print(num_edges)# Check if the graph is directedis_directed = nx.is_directed(g)print(is_directed)Once we have converted the networks into graph objects, we can apply various functions on them and visualize the networks using the networkx package. For example, let’s visualize the karate network:
# Read the networkg = nx.read_gml(“karate.gml”)# Visualize the networknx.draw(g, with_labels=True)plt.show()There is a number of nodes and edges in an undirected graph. To visualize this graph, we can use the function nx.draw() and the parameter plt.show(). This allows us to see the graph and interact with it by moving and zooming. We can also save the figure.
For a directed graph, the arrows represent the edges. We can still zoom and analyze specific parts of the graph.
There are different layouts available in NetworkX, such as circular, spectral, and spring layout. Each layout arranges the nodes and edges in a different way.
We can perform basic analysis on the graph, such as checking the degree distribution. Degree distribution tells us how many nodes have a particular degree. We can plot the degree distribution to get a better understanding of the graph.
In the code, we create a function to plot the degree distribution of the graph. We use NetworkX functions to get the degrees of each node and calculate the unique degrees. Then, we can determine how many nodes have each degree.