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.

本研究介绍了使用图神经网络（GNN）来预测伦敦某一天的天气和星期几，使用桑坦德自行车共享系统的数据集作为图分类任务。我们的模型在土地利用特征、自行车站周围的家庭数量以及城市温度等图特征的基础上，通过引入训练节点嵌入的图特征拼接运算符和以地理相邻性为基础的图粗化运算符「空间图粗化」，在交叉熵损失和验证数据集准确率上优于基准模型。