Fixing 'RuntimeError: index 21 is out of bounds...' in PyTorch Geometric GCN

The error message 'RuntimeError: index 21 is out of bounds for dimension 0 with size 20' indicates an issue in your graph data, specifically with the 'edge_index' tensor. This error often occurs when using the GCNConv.forward() method within PyTorch Geometric during graph convolutional network (GCN) training or inference.

The root cause is that the edge_index tensor, which represents the connections between nodes in your graph, contains indices that exceed the valid range for the number of nodes in your graph. This means some edges are pointing to nodes that don't exist, causing the error.

Here's a breakdown of the issue and a solution:

Understanding the Error

The error happens in the gcn_norm() function, which is used to normalize the adjacency matrix of your graph for efficient graph convolution calculations. This function uses the scatter() operation, which aggregates values based on indices. When an index is out of bounds, it leads to this runtime error.

Debugging and Solution

1. Inspect the edge_index Tensor:

   • Carefully examine the values in your edge_index tensor. Ensure that all indices are within the valid range (0 to num_nodes - 1).
   • Print or visualize the tensor to identify any indices that are too large.

2. Check for Off-by-One Errors:

   • Sometimes, indexing errors are due to off-by-one issues. Ensure that you're correctly accounting for the starting and ending indices of your node data and edge connections.
   • For example, if you have 20 nodes, the maximum valid index in edge_index should be 19.

3. Verify Data Loading and Preprocessing:

   • Double-check how you're loading your graph data and if any preprocessing steps might be modifying the edge_index tensor incorrectly.
   • Ensure that your dataset class is correctly generating the edge_index tensor with appropriate indices for the graph's nodes.

Example Code Snippet (Illustrative):

import torch
from torch_geometric.data import Data

# Example graph with 5 nodes
num_nodes = 5

# Correct edge_index
edge_index = torch.tensor([[0, 1, 2, 3, 4], [1, 2, 3, 4, 0]], dtype=torch.long)

# Incorrect edge_index (Index 5 is out of bounds)
# edge_index = torch.tensor([[0, 1, 2, 3, 5], [1, 2, 3, 4, 0]], dtype=torch.long)

x = torch.randn(num_nodes, 10)  # Node features

data = Data(x=x, edge_index=edge_index)

print(data)  # Inspect the data object

Key Points:

• The error usually arises from a discrepancy between the number of nodes in your graph and the indices used in the edge_index tensor.
• Thoroughly examine the data loading, preprocessing, and graph construction steps to identify the source of the incorrect indices.
• By ensuring that the indices in edge_index accurately represent the connections between your nodes, you can fix this out-of-bounds error and continue your graph neural network training or inference.