Weifanwong / MEgo2Vec-Embedding-Matched-Ego-Networks-for-User-Alignment-Across-Social-Networks

CIKM'18

MEgo2Vec: Embedding Matched Ego Networks for User Alignment Across Social Networks∗

This is basic implementation of our CIKM'18 paper:

Jing Zhang, Bo Chen, Xianming Wang, Hong Chen*, Cuiping Li, Fengmei Jin, Guojie Song, and Yutao Zhang. 2018. MEgo2Vec: Embedding Matched Ego Networks for User Alignment Across Social Networks. In Proceedings of ACM conference (CIKM’18).

Requirements

• Ubuntu 16.04
• Python 2.7
• Tensorflow-gpu
• GPU,CUDA,CUDNN

Note: Running this project will consume upwards of 20GB hard disk space. The overall pipeline will take several hours. You are recommended to run this project on a Linux server.

Data Description

Training data in this demo is about AMiner - Linkedin networks which is placed in the data directory. If you want to download the original network data (AMiner, Linkedin), please use the link : https://pan.baidu.com/s/1b6_8jd8J9CGiCpyFBfZgoQ 密码:xacn . If you want to get other networks (Twitter, MySpace LastFm...), please click the link.

How to run

cd code
python main.py

Note: Hyper parameter and training data in this demo is a little different than what we used in the experiments, so the performance (F1-score) will be a little bit lower than reported scores.