Repository for training a Temporal Graph Neural Network model for predicting frequency of keywords (node regression)

Task description:

• Based on .csv data containing keywords appearing together in scientific journals for specific months of an 8-year period (2014-2021), train model to predict the future frequency (e.g. for the next year) of the same keywords
• The main steps involve:
  1. Creating data windows of one year (8 data windows in total)
  2. Creating graphs for each data window in order to leverage structural dependencies between keywords (e.g. co-occurence)
  3. Transforming graphs into proper format in order to be ingested by Temporal GNN model
  4. Training the model

To train the model:

1. First run python generate_graphs.py to generate the graph data
   • This will create a graphs.pkl file
2. Then run python training.py to train the model using the generated graphs
   • This will save the best model parameters based on evaluation loss

You can evaluate the model and generate predictions using the evalutation.ipynb notebook