chengw07 / netra

Code for "Learning Deep Network Representations with Adversarially Regularized Autoencoders."

NetRA

Code for "Learning Deep Network Representations with Adversarially Regularized Autoencoders."

NetRA Documentation ---pytorch version Wei Cheng([email protected]) This is the document of NetRA for pytorch version. For technical details, please refer to the paper: Learning Deep Network Representations with Adversarially Regularized Autoencoders. Wenchao Yu, Cheng Zheng, Wei Cheng, Charu Aggarwal, Dongjing Song, Bo Zong, Haifeng Chen, Wei Wang. The Proceedings of the ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (SIGKDD’18), 2018.

The entry of running is ./src/train.py. It conducts embedding of nodes in a static network (karate network) and visualizes the 2-dimensional embedding results of nodes. The html docs for each python file are included in ./doc_html. To better understand the framework, the reader is supposed to be familiar with pytorch framework and Wasserstein GAN.

System Requirements

We need python 2.7 and torch 0.3.1.

1). Install using the Homebrew package manager:

Mac:

/usr/bin/ruby -e "$(curl -fsSL https://raw.githubusercontent.com/Homebrew/install/master/install)" export PATH="/usr/local/bin:/usr/local/sbin:$PATH"

brew update

brew install python@2 # Python 2

sudo pip install -U virtualenv # system-wide install

Ubuntu:

sudo apt update

sudo apt install python-dev python-pip

sudo pip install -U virtualenv # system-wide install

2). Create a new virtual environment by choosing a Python interpreter

virtualenv --system-site-packages -p python2.7 ./venv

source ./venv/bin/activate

pip install --upgrade pip

3). install torch version 0.3.1

pip install torch==0.3.1

4). install packages

pip install networkx

pip install scipy

netra's Issues

Code in model.py at line 229

Nice work~

In your model.py in line 227-229, why forward but not return? What's the logic here?

227     def embed_after_LSTM(self, dic, length):                                   
228         embeddings = self.forward(dic, length, noise = False)                   
229         return self.emb_nhidden  

tensorflow implement of the code

hi, if you have , could you give me your tensorflow implement of the code,thank you ~