Implementing the model in Python
In the following step, we will load in the data that we are going to be working with.
To begin creating this graph in Python, we can import the nodes from musae_facebook_target.csv, using the standard Python csv library:
import csv
with open('./data/facebook_large/musae_facebook_target.csv', 'r', encoding='utf-8') as csv_file:
reader = csv.reader(csv_file)
data = [line for line in reader]
print(data[:10])
print(len(data))
Here, we open the CSV file with utf-8 encoding, as some node name strings contain non-standard characters. We use csv.reader to read the file, and convert this into a list of lists with a list comprehension (a special construct to encapsulate a loop inside a list to return a new list based on the loop logic, in essence to create a list from another list). Finally, we confirm that the CSV file is loaded correctly by examining the first few lines and checking the length of the imported list, which...