Graph Classification with Transformers — Graphormer Model

AI Impact Summary

This tutorial demonstrates the use of the Graphormer model from the Transformers library for graph classification, leveraging the OGB (Open Graph Benchmark) dataset. The process involves loading the dataset, preprocessing it with Graphormer's default preprocessing steps to generate features like in/out degree and shortest path matrices, and then fine-tuning a pretrained Graphormer model for binary classification. This approach highlights a practical workflow for applying transformer-based techniques to graph data, offering a starting point for exploring more complex graph-related machine learning tasks.