Name Disambiguation using Network Embedding

This repository provides a reference implementation of name disambiguation using network embedding as described in the paper:

Name Disambiguation in Anonymized Graphs using Network Embedding.
Baichuan Zhang and Mohammad Al Hasan.
Proceedings of the 2017 ACM on Conference on Information and Knowledge Management (CIKM 2017)
https://arxiv.org/pdf/1702.02287.pdf

Pre-Requisite

• Python 2.7
• Numpy
• Networkx
• scikit-learn

Basic Usage

Example

To run disambiguation embedding code, execute the following command from the project home directory:
python embedding_model/main.py sampled_data/data.xml 20 0.02 0.005 100 'uniform'

Options

You can check out the hyper-parameter options using:
python embedding_model/main.py --help

Output

The output is Macro-F1 result and ranking loss value in each epoch. In the meanwhile, we also generate final embedding file in emb/ directory, which contains n+1 lines for the document collection with n documents. The first line has the following format:

num_of_docs dim_of_representation

The next n lines are as follows:

doc_id dim1 dim2 ... dimd

where dim1, ... , dimd is the d-dimensional representation learned by the proposed embedding model.

Reference

If you find this work useful for your research, please consider citing the following paper:

@inproceedings{zhang-cikm2017,
author = {Zhang, Baichuan and Al Hasan, Mohammad},
 title = {Name Disambiguation in Anonymized Graphs using Network Embedding},
 booktitle = {Proceedings of the ACM on Conference on Information and Knowledge Management (CIKM)},
 year = {2017}
}