WSDM-2022-Challenge

IDEAS Lab UT's submission to the WSDM 2022 Temporal Link Prediction challenge.

Generate Predictions

To generate our predictions, run the following two Jupyter notebooks:

1. construct_features.ipynb: This will construct the edge features for the final test set and save them to CSV.
2. predict_edges.ipynb: This will train a logistic regression model on the edge features and generate predictions on the final test set.

The feature construction may take a few hours, so we have included also the constructed features with the filenames beginning with featureA_ and featureB_ so you can directly run predict_edges.ipynb to generate predictions from our pre-computed features.

Input Files

The following input data files are assumed to be in the root directory:

Dataset A

• edges_train_A.csv
• node_features.csv
• edge_type_features.csv
• input_A_initial.csv
• input_A.csv

Dataset B

• edges_train_B.csv
• input_B_initial.csv
• input_B.csv

Output Files

Predictions will be generated also in the root directory:

• output_A.csv
• output_B.csv

Dependencies

See requirements.txt for required packages. The code for the CHIP model is included in the directory CHIP-Network-Model, which is added to the system path when loading chip_features.py.