- 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
- python 35
- scikit-learn==0.21.3 (specific version for STNE)
- numpy
- tqdm
- networkx
- pandas
- 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
- texttable
- OS: Only Mac OS and Linux are available for this code.
- tensorflow==1.1
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