DIVINE

• The repository is the implementation of DIVINE Directed Network Embedding with Virtual Negative Edges {Hyunsik Yoo, Yeon-Chang Lee,} Kijung Shin, Sang-Wook Kim 15th ACM International Conference on Web Search and Data Mining (WSDM), 2022

Online Apendix

Requirements

• python 35
• scikit-learn==0.21.3 (specific version for STNE)
• numpy
• tqdm
• networkx
• pandas

For WRMF:

• tensorflow==1.13.1
• Cython

go to './NeuRec' and compile the evaluator of cpp implementation with the following command line:

python setup.py build_ext --inplace

For STNE:

• texttable

For SIDE:

• OS: Only Mac OS and Linux are available for this code.
• tensorflow==1.1

(Please refer to the author's original README.md for more details of WRMF, STNE, and SIDE.)

Usage

python divine.py --dataset GNU --emb_algo stne --lp_task LP-uniform --num_embed 128 --vne_algo wrmf --theta 0.5 --selection_strategy local

• vne_algo: Method for inferring degree of negativity
• selection_strategy: Strategey for selcting VNEs
• theta: hyperparamter for determining the number of VNEs to be added
• dataset: input (unsigned) network
• emb_algo: (signed) network embedding method for learning node embeddings
• num_embed: dimensionality of embeddings
• lp_task: link prediction task type