this is the code of the following paper: Qiao, Ziyue, Yi Du, Yanjie Fu, Pengfei Wang, and Yuanchun Zhou. "Unsupervised Author Disambiguation using Heterogeneous Graph Convolutional Network Embedding." In 2019 IEEE International Conference on Big Data (Big Data), pp. 910-919. IEEE, 2019.
If this code helps you, please cite this paper.
- python 3.6.5
- networkx 1.9.1
- gensim 3.4.0
- sklearn 0.20.1
- numpy 1.14.3
- pandas 0.23.0
- tensorflow 1.14.0
you should first unzip the file "experimental-results.zip", then create new folders named "gene" and "result". One pre-trained word2vec model from the python-gensim library is needed, and you should put it into the folder "gene".
you are recommended to use the word2vec model we pre-trained to generate word embeddings of publication titles, you can find it in OneDrive (or BaiduYun). Or you can train your own word vectors(dimension = 100) using the word2vec method in gensim library.