Run sequentially:

python create_followSet.py
python construct_features.py {start_date} {end_date}
python prediction.py {start_date} {end_date} > results/prediction_task.txt
python degree_precision.py {start_data} {end_date}

where start_date and end_date are arguments that define the time window we want to use in our dataset
e.g. for using the whole dataset feb 1- feb 28 we have to run:

python create_followSet.py
python construct_features.py 1 28 
python prediction.py 1 28 > results/prediction_task.txt
python degree_precision.py 1 28

We have created a jupyter notebook (called read_results.ipynb ), which reads the output of the prediction.py script (which is saved in a txt file under "results/prediction.txt") and a pickle file, where we have saved the prediction accuracy per embeddedness results and reproduces the main figures and plots from our paper.

Important!

1. Triangles were listed using MACE package Our scripts require a MACE executable inside a "mace/" folder.
2. code is written in Python2.7 and the scikit-learn version used in the experiments is 0.20.2.
3. Dataset is anonymized, so given user IDs do not represent real twitter users.