Evolving Bipartite Model

We build an Evolving Bipartite Model (EBM) to reveals the bounded weights in social networks by launching a case study in recommendation networks.

If you use the code, please cite the following paper:

@article{TMC.2019-07-0462,
author = {Jiaqi Liu, Cheng Deng, Luoyi Fu, Huan Long, Xiaoying Gan, Xinbing Wang, Guihai Chen and Jun Xu},
title = {Evolving Bipartite Model Reveals the Bounded Weights in Mobile Social Networks: A Case Study in Recommendation Networks},
year = {2019}
journal = {IEEE Transactions on Mobile Computing}
}

This package is mainly contributed by Cheng Deng and Lingkun Kong, Guided by Jiaqi Liu, Luoyi FU and Xinbing Wang.

Introduction

1. validation folder contains codes for analysis in 10 social network datasets, including 6 recommendation networks, 3 scholarly networks and 1 facebook messege social networks. The introduction links for these datasets are provided here:

   ​ a. Amazon Datasets including CDs, Movies, Electronic and Books.

   ​ b. Audioscrobbler

   ​ c. BookCrossing

   ​ d. Acemap Academic Knowledge Graph

   ​ e. Facebook-like Forum Messeage Network

   if you want to download the dataset, you can refer to my Google Drive.

2. real_evol folder contains codes for analysis amazon data's evolving properties.

3. simulation folder contains codes for simulation of two algorithm, proposed by us.

4. simulation_evol folder contains codes for analysis simulational data's evolving properties.

5. regression folder contains codes for datasets's alpha regression with the change of boundry calculating by the degrees.