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Colagnn

This is the source code for paper Cola-GNN: Cross-location Attention based Graph Neural Networks for Long-term ILI Prediction appeared in CIKM2020 (research track)

Raw Data

The raw dataset are in in the data folder. For each dataset, there are two files defined. For example, for the Japan-prefecture dataset, we have two files:

• japan.txt includes the spatiotemporal data. Columns indicate locations (i.e., prefecture) and rows indicate timestamps (i.e., weeks). Each value is the number of patients in a location at a time point. The data are arranged in chronological order.
• japan-adj.txt contains a adjacency matrix.

Training Data

The training data are processed by the DataBasicLoader class in the src/data.py file. We can set different value for historical window size args.window and horizon/leadtime args.horizon. Setting args.window=20, args.horizon=1/2 means using data from the previous 20 weeks to predict the upcoming/next week. There are some functions in this class:

• _split splits the data into training/validation/test sets.
• _batchify generates data samples. Each sample contains a time series input with length equal to args.window, and a value for the output. For the current code, there are overlaps in the inputs of different samples.
• get_batches generates random mini-batches for training.