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.

翻译：本研究提出使用图神经网络（GNN）对伦敦Santander Cycles共享单车系统数据集进行天气与星期预测，将其构建为图分类任务。所提出的GNN模型创新性地引入：（i）图特征与训练节点嵌入的拼接算子，以及（ii）基于地理邻接性的图粗化算子，即"空间图粗化"。通过将自行车站点周边的土地利用特征与住户数量作为节点特征，并结合城市温度作为图特征，所提模型在验证数据集的交叉熵损失与准确率指标上均优于基线模型。