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