Introduction to Graph Machine Learning/

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Approach to the Problem

Approach to the Problem

Learn how to solve a node classification problem.

We'll cover the following...

Our approach

We use the PyTorch Geometric library to train the GNN problem, which implements several algorithms.

The first step in this approach is to build a data handler. There are a few methods provided in torch_geometric.data for this purpose.

Custom data handler

Let's take a look at the following code in which we create a custom data handler for our graph data using the PyTorch Geometric library:

Python 3.10.4
import torch
from torch_geometric.data import Data
import pandas as pd
from sklearn.model_selection import train_test_split
# create edge index of the graph
edge_index = torch.tensor(list(G.edges()), dtype=torch.long).t().contiguous()
# create the edge weight tensor
edge_weights = [G[u][v]['contact'] for u, v in list(G.edges())]
edge_weight = torch.tensor(edge_weights, dtype=torch.float)
# create node features 
# Create a dataframe from the graph nodes
df = pd.DataFrame(dict(G.nodes(data=True))).T
# convert selected features to tensor
node_features = torch.tensor(df[['tested','symptoms',
                                 'vaccinated','mobility']].astype(float).values,
                             dtype=torch.float)
# labels
node_labels = df.label.map({'infected': 1, 'not infected': 0})
y = torch.from_numpy(node_labels.values).type(torch.long)
# create train and test masks
X_train, X_test, y_train, y_test = train_test_split(pd.Series(G.nodes()), 
                                                    node_labels,
                                                    stratify = node_labels,
                                                    test_size=0.20, 
                                                    random_state=56)
n_nodes = G.number_of_nodes()
train_mask = torch.zeros(n_nodes, dtype=torch.bool)
test_mask = torch.zeros(n_nodes, dtype=torch.bool)
train_mask[X_train.index] = True
test_mask[X_test.index] = True
# create torch_geometric Data object
data = Data(x=node_features, edge_index=edge_index, edge_weight=edge_weight,
            y=y, train_mask=train_mask, test_mask=test_mask,
            num_classes = 2, num_features=len(node_features))
print(data)

Let’s look at the code explanation below:

  • Line 7: Creates an edge index of the graph, which is the default input used in this library.

  • Lines 10–11: Create an edge weight tensor using the contact details of the graph.

  • Line 15: Creates a DataFrame of all the node features.

  • Lines 18–20: Select relevant features and convert them into a PyTorch tensor.

  • Line 23–24: Create a tensor of node labels and change the categorical variables into numerical ones.

  • Lines 27–31: Split the nodes and labels into training and testing sets in a ratio of 80:20 using a stratified split. This ensures equal proportions of infected and not infected cases in both sets. ...