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This study introduced the use of Graph Neural Network (GNN) for predictingthe weather and weekday of a day in London, from the dataset of SantanderCycles bike-sharing system as a graph classification task. The proposed GNNmodels newly introduced (i) a concatenation operator of graph features withtrained node embeddings and (ii) a graph coarsening operator based ongeographical contiguity, namely "Spatial Graph Coarsening". With the nodefeatures of land-use characteristics and number of households around the bikestations and graph features of temperatures in the city, our proposed modelsoutperformed the baseline model in cross-entropy loss and accuracy of thevalidation dataset.

Spatial Graph Coarsening: Weather and Weekday Prediction with London's Bike-Sharing Service using GNN