Setting up the environment

Before we dive into the implementation of our graph deep learning models, it’s crucial to set up our development environment with the necessary libraries and tools. In this section, we’ll cover the essential imports and explain their roles in our project.

First, let’s start by importing the required libraries:

import torch
import torch_geometric
from torch_geometric.data import Data
from torch_geometric.nn import GCNConv, GAE
from torch_geometric.utils import train_test_split_edges
from torch_geometric.transforms import RandomLinkSplit
from sklearn.manifold import TSNE
from sklearn.cluster import KMeans
import matplotlib.pyplot as plt
import networkx as nx
from sklearn.metrics import roc_auc_score

Let’s break down these imports and their purposes:

• torch: The core PyTorch library used for tensor operations and building neural networks
• torch_geometric: PyG, an extension library to PyTorch for deep learning on...