Jump into the buzzing world of Twitter! 🐦 In this project, you'll explore the fascinating network of Twitter connections and uncover the hidden patterns within one of the most popular social networks out there. You'll get hands-on with real Twitter follower data using Pandas, transforming it into a cool directed graph with NetworkX. Along the way, you'll learn how to spot key influencers, find out who follows who, and discover mutual connections between users.
The Data
twitter-followers.csv
| Column | Description |
|---|---|
'FOLLOWER' | id of the user who is following |
'FOLLOWEE' | id of the user being followed |
Throughout the project, you will manipulate and organize this data to extract meaningful insights.
Using the Twitter dataset, you'll implement several useful functions which will aid in the analysis of Twitter's user-base.
Implement the following functions:
is_following(T, user_id1, user_id2): Returns True if user_id2 is following user_id1, else False.
get_users_following_min_accounts(T, min_following_count): Returns a list of user IDs who are following at least min_following_count accounts.
get_mutual_followees(T, user_id1, user_id2): Returns a list of user ids that user_id1 and user_id2 both follow.
get_most_connected_user(T): Returns the user id of the most connected user (the one with the most followers/followees).
# Imports you'll need for the project
import pandas as pd
import networkx as nx
# Load csv data and store as edgelist (a directed graph)
df = pd.read_csv('twitter-followers.csv')
T = nx.from_pandas_edgelist(df, 'FOLLOWER', 'FOLLOWEE', create_using=nx.DiGraph())
# Display head of the DataFrame
print(df.head())
# Returns True is user_id2 is following user_id1
def is_following(T, user_id1, user_id2):
return user_id1 in T.neighbors(user_id2)
# Returns a list of user ids that are following at least min_following_count other users
def get_users_following_min_accounts(T, min_following_count):
return [node for node in T.nodes() if len(list(T.neighbors(node))) >= min_following_count]
# Returns a list of user ids that user_id1 and user_id2 both follow
def get_mutual_followees(T, user_id1, user_id2):
# Get the list of followees for both users
followees_user1 = list(T.neighbors(user_id1))
followees_user2 = list(T.neighbors(user_id2))
# Find mutual followees using a loop
mutual_followees = []
for user in followees_user1:
if user in followees_user2:
mutual_followees.append(user)
return mutual_followees
# Returns the user id of the most connected user (the one with the most followers/followees)
def get_most_connected_user(T):
# Calculate degree centrality for each node
degree_centrality = nx.degree_centrality(T)
# Initialize variables to track the maximum centrality and corresponding node
max_centrality = -1
most_connected_user = None
# Loop through the dictionary to find the node with the highest centrality
for node, centrality in degree_centrality.items():
if centrality > max_centrality:
max_centrality = centrality
most_connected_user = node
return most_connected_user