Spatial Graph Coarsening: Weather and Weekday Prediction with London's
  Bike-Sharing Service using GNN
Yuta Sato Pak Hei Lam Shruti Gupta Fareesah Hussain
This study introduced the use of Graph Neural Network (GNN) for predicting
the weather and weekday of a day in London, from the dataset of Santander
Cycles bike-sharing system as a graph classification task. The proposed GNN
models newly introduced (i) a concatenation operator of graph features with
trained node embeddings and (ii) a graph coarsening operator based on
geographical contiguity, namely "Spatial Graph Coarsening". With the node
features of land-use characteristics and number of households around the bike
stations and graph features of temperatures in the city, our proposed models
outperformed the baseline model in cross-entropy loss and accuracy of the
validation dataset.