Implementation of SetEvolve, CIKM 2019.

Please cite the following work if you find the code useful.

@inproceedings{yang2019setevolve,
	Author = {Yang, Carl and Gan, Lingrui and Wang, Zongyi and Shen, Jiaming and Xiao, Jinfeng and Han, Jiawei},
	Booktitle = {CIKM},
	Title = {Query-specific knowledge summarization with entity evolutionary networks},
	Year = {2019}
}

Contact: Carl Yang (yangji9181@gmail.com)

Publication

Carl Yang, Lingrui Gan, Zongyi Wang, Jiaming Shen, Jinfeng Xiao, Jiawei Han, "Query-Specific Knowledge Summarization with Entity Evolutionary Networks", CIKM19.

Content

Files:

• BaseGraphicalLasso.py - Base class for graphical lasso
• StaticGL.py - GL solver solving time independent graphical lasso problems
• DataHandler.py - Results writer
• penalty_functions.py - Penalty functions
• SetEvolve.py - Main algorithm

*BaseGraphicalLasso.py, StaticGL.py, DataHandler.py, penalty_functions.py are adopted from:
Time-Varying Graphical Lasso, https://github.com/tpetaja1/tvgl

Folders:

• case_study - input folder, time dependent observations
• network_results - output folder from the algorithm, network inferences

Deployment

Implemented in Python2, with numpy, scipy and multiprocessing

Demo

Run the following command

python SetEvolve.py case_study/bio_1991_2003.csv 2 4 20 0

Parameter

1. filename
2. discrete param (discrete=2, otherwise=1)
3. number of blocks
4. lambda
5. output result matrix(optional, 0 as default), x means the xth network inference will be output as adjacency list

Input

See folder case_study, an input file contains:

1. one line of entities
2. followed by multiple lines of time dependent observations (the first column is just timestamp and not used for computation)

Output

• result_draw.json - the specified network inference output, for the purpose of easy drawing
• network_results - output folder from the algorithm, network inferences

Contact

If you have any questions about the code or data, please feel free to contact me.