Towards Quantifying Long-Range Interactions in Graph Machine Learning: a Large Graph Dataset and a Measurement

1. 1Identify long-range dependencies in graph-structured data
2. 2Develop a large graph dataset to capture these dependencies
3. 3Propose a measurement to quantify long-range interactions
4. 4Evaluate graph machine learning models using the measurement

AI engineers and researchers on a team can benefit from this work to improve graph representation learning, while data scientists can apply the measurement to evaluate model performance

💡 Long-range dependencies are crucial for effective graph representation learning

📈 New dataset and measurement for quantifying long-range interactions in graph ML!