Summary
This chapter focused on a traffic forecasting task using T-GNNs. First, we explored the PeMS-M dataset and converted it from tabular data into a static graph dataset with a temporal signal. In practice, we created a weighted adjacency matrix based on the input distance matrix and converted the traffic speeds into time series. Finally, we implemented an A3T-GCN model, a T-GNN designed for traffic forecasting. We compared the results to two baselines and validated the predictions made by our model.
In Chapter 16, Building a Recommender System Using LightGCN, we will see the most popular application of GNNs. We will implement a lightweight GNN on a massive dataset and evaluate it using techniques from recommender systems.
