This code serves as an implementation of the work on calculating keywords and their emerging importance outlined here: https://people.cs.clemson.edu/~isafro/papers/dynamic-centralities.pdf

The results in the paper have been replicated on the Boston dataset using time intervals of 60 and 15 minutes, located in boston_examples.

1. All code is run in Python 3.6 (Anaconda 4.3.0)
2. Data to be processed should be stored in ordered text files (i.e., file1.txt, file2.txt, ... fileN.txt for N intervals, or some other numbered format.)
3. Text files should contain one-document (i.e., one tweet) per line

• Ensure all requirements are satisfied. The program can be run as follows.

# after repo has been downloaded
cd dynamic_eigenvector_centralities
pip install requirements.txt
python dec_main.py --input_folder /home/username/time_series_data/ --P 6 --output_folder /home/username/dec_results/

• dec_main.py Runs the full algorithm to compute DEC values described in the
• dec_graph.py contains code for the graph logic of the algorithm
• dec_text.py contains code for preprocessing and cleaning the data
• break_files.py a useful script for dividing time-series CSV data