Directed vs undirected graphs in Data Structures Theory - Hands-On Comparison

Understanding Directed vs Undirected Graphs

📖 Scenario: You are helping a friend understand how different types of graphs work by creating simple examples of directed and undirected graphs. These graphs represent connections between places in a small town.

🎯 Goal: Build two simple graph examples: one directed and one undirected. This will help visualize how connections differ when direction matters versus when it does not.

📋 What You'll Learn

Create a dictionary called undirected_graph representing an undirected graph with exact nodes and edges

Create a dictionary called directed_graph representing a directed graph with exact nodes and edges

Add a variable called node_count that counts the total number of nodes in the graphs

Add a variable called edge_count that counts the total number of edges in the directed graph

💡 Why This Matters

🌍 Real World

Graphs are used to model networks like roads, social connections, and computer networks where direction of connection matters or not.

💼 Career

Understanding graph types is important for roles in software development, data science, and network engineering.

1

Create an undirected graph

Create a dictionary called undirected_graph with these exact connections: 'A': ['B', 'C'], 'B': ['A', 'D'], 'C': ['A'], and 'D': ['B']. This represents an undirected graph where edges go both ways.

Data Structures Theory

# Create the undirected graph dictionary
# Your code here

Need a hint?

Think of each key as a place and the list as places directly connected to it without direction.

2

Create a directed graph

Add a dictionary called directed_graph with these exact connections: 'A': ['B', 'C'], 'B': ['D'], 'C': [], and 'D': ['A']. This represents a directed graph where edges have direction from the key to the nodes in the list.

Data Structures Theory

undirected_graph = {
    'A': ['B', 'C'],
    'B': ['A', 'D'],
    'C': ['A'],
    'D': ['B']
}
# Create the directed graph dictionary
# Your code here

Need a hint?

Remember, in a directed graph, connections only go one way from the key to the listed nodes.

3

Count the total nodes

Create a variable called node_count that stores the total number of unique nodes in the graphs. Use the keys of the undirected_graph dictionary to count nodes.

Data Structures Theory

undirected_graph = {
    'A': ['B', 'C'],
    'B': ['A', 'D'],
    'C': ['A'],
    'D': ['B']
}
directed_graph = {
    'A': ['B', 'C'],
    'B': ['D'],
    'C': [],
    'D': ['A']
}
# Count the total nodes
# Your code here

Need a hint?

Use the len() function on the keys of the undirected graph dictionary.

4

Count the total edges in the directed graph

Create a variable called edge_count that stores the total number of edges in the directed_graph. Calculate this by summing the lengths of all adjacency lists in directed_graph.

Data Structures Theory

undirected_graph = {
    'A': ['B', 'C'],
    'B': ['A', 'D'],
    'C': ['A'],
    'D': ['B']
}
directed_graph = {
    'A': ['B', 'C'],
    'B': ['D'],
    'C': [],
    'D': ['A']
}
node_count = len(undirected_graph.keys())
# Calculate total edges in directed_graph
# Your code here

Need a hint?

Use a generator expression inside sum() to add up the lengths of all adjacency lists.