Graphs in social networks in Data Structures Theory - Mini Project: Build & Apply

Graphs in Social Networks

📖 Scenario: Imagine you are analyzing a small social network where people are connected as friends. Each person is a node, and each friendship is an edge connecting two nodes.

🎯 Goal: You will build a simple representation of a social network using a graph structure. You will create the data, set a threshold for popular users, find users with many friends, and finally mark those popular users.

📋 What You'll Learn

Create a dictionary representing the social network graph with exact people and their friends

Add a variable for the minimum number of friends to be considered popular

Use a loop to find all popular users based on the threshold

Add a final dictionary marking popular users with a boolean value

💡 Why This Matters

🌍 Real World

Social networks use graphs to represent connections between people, helping to analyze relationships and suggest new friends.

💼 Career

Understanding graph structures is important for roles in data analysis, social media management, and software development involving network data.

1

Create the social network graph

Create a dictionary called social_graph with these exact entries: 'Alice': ['Bob', 'Charlie'], 'Bob': ['Alice', 'David', 'Eve'], 'Charlie': ['Alice'], 'David': ['Bob'], 'Eve': ['Bob'].

Data Structures Theory

# Create the social_graph dictionary with the given people and their friends
# Your code here

Need a hint?

Use a dictionary where keys are names and values are lists of friends.

2

Set the popularity threshold

Create a variable called popularity_threshold and set it to 2 to represent the minimum number of friends needed to be considered popular.

Data Structures Theory

social_graph = {
    'Alice': ['Bob', 'Charlie'],
    'Bob': ['Alice', 'David', 'Eve'],
    'Charlie': ['Alice'],
    'David': ['Bob'],
    'Eve': ['Bob']
}
# Create popularity_threshold variable and set it to 2
# Your code here

Need a hint?

This variable helps decide who is popular based on friend count.

3

Find popular users

Create an empty list called popular_users. Use a for loop with variables user and friends to iterate over social_graph.items(). Inside the loop, if the length of friends is greater than or equal to popularity_threshold, append user to popular_users.

Data Structures Theory

social_graph = {
    'Alice': ['Bob', 'Charlie'],
    'Bob': ['Alice', 'David', 'Eve'],
    'Charlie': ['Alice'],
    'David': ['Bob'],
    'Eve': ['Bob']
}
popularity_threshold = 2
# Create popular_users list and find users with friends count >= popularity_threshold
# Your code here

Need a hint?

Check each user's friend list length and add to popular_users if it meets the threshold.

4

Mark popular users in a dictionary

Create a dictionary called popularity_status using a dictionary comprehension. For each user in social_graph, set the value to true if user is in popular_users, otherwise false.

Data Structures Theory

social_graph = {
    'Alice': ['Bob', 'Charlie'],
    'Bob': ['Alice', 'David', 'Eve'],
    'Charlie': ['Alice'],
    'David': ['Bob'],
    'Eve': ['Bob']
}
popularity_threshold = 2
popular_users = []
for user, friends in social_graph.items():
    if len(friends) >= popularity_threshold:
        popular_users.append(user)
# Create popularity_status dictionary marking popular users as true, others as false
# Your code here

Need a hint?

Use a dictionary comprehension to assign true or false for each user.