This is implementation of our KDD'18 paper:

Yutao Zhang, Fanjin Zhang, Peiran Yao, and Jie Tang. Name Disambiguation in AMiner: Clustering, Maintenance, and Human in the Loop. In Proceedings of the Twenty-Forth ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD'18).

• Linux
• python 3
• install requirements via pip install -r requirements.txt

Note: Running this project will consume upwards of 10GB hard disk space. The overall pipeline will take several hours. You are recommended to run this project on a Linux server.

Please download data here (or via OneDrive). Unzip the file and put the data directory into project directory.

cd $project_path
export PYTHONPATH="$project_path:$PYTHONPATH"
python3 scripts/preprocessing.py
# global model
python3 global_/gen_train_data.py
python3 global_/global_model.py
python3 global_/prepare_local_data.py
# local model
python3 local/gae/train.py
# estimate cluster size
python3 cluster_size/count.py

Note: Training data in this demo are smaller than what we used in the paper, so the performance (F1-score) will be a little bit lower than reported scores.