CS224W - Bag of Tricks for Node Classification with GNN - Label Usage

[chriskynguyen]

Add label_usage.py to torch_geometric.nn.models and unit test.

Part of #9831 for our final project for the Stanford CS224W course, this implements Label Usage as described in “Bag of Tricks for Node Classification with Graph Neural Networks”.

Description of Label Usage

• Label usage utilizes true labels as features and learns to predict other labels.
• Within label usage, label reuse is done to recycle predicted soft labels of previous iterations and uses the labels as input
• When in training, label usage performs a split on the training nodes and predicts all other unlabeled nodes
• In evaluation, label usage doesn't perform split but uses true labels for training nodes and predicts on validation and test nodes
• The base model should have input dimensions of num_features + num_classes to accommodate concatenation of labels with features

Benchmark

Run on Arxiv (ogbn-arvix) dataset with 100 epochs, 10 recycling iterations, and a split ratio of 0.6 using a GAT model.
Code used to test Label Usage Colab Notebook.

Dataset	Val Accuracy (%)	Test Accuracy (%)
Arxiv	69.32	68.53