DynG2G: An Efficient Stochastic Graph Embedding Method for Temporal Graphs

This repository contains the code for DynG2G: An Efficient Stochastic Graph Embedding Method for Temporal Graphs by Mengjia Xu, Apoorva Vikram Singh and George Em Karniadakis; This work has been submitted to IEEE Transactions on Neural Networks and Learning Systems (under revision). (The code was developed by Apoorva Vikram Singh and Mengjia Xu)

Setting up the Environment to run the code

To create an environment to run the code, execute the shell file on the terminal using:

sh ./env_create.sh

The new environment called DynG2G is created. It will include all the dependencies needed to run the code. Activate it using:

source activate DynG2G

Downloading the data to run the code

To download all datasets used in the experiment, run the shell file:

sh ./download_data.sh

To download the datasets seperately:

AS dataset: Download link
SBM dataset: Download link
Bitcoin-OTC dataset: Download link
UCI dataset: Download link
Slashdot: Download link
Facebook: Download link
Reality Mining: Download link
Digg: Download link

Generating the embeddings

To generate the embeddings for AS dataset, run the command:

python Generate_Embeddings/AS_embed.py -f configs/[configFile]

Similarily, the embeddings for other datasets can be generated by running their corresponding files in Generate_Embeddings folder along with the desired configuration files (config).

You can simply skip this process and download the embeddings from here.

Evaluating the embeddings

To evaluate the embeddings for AS dataset run this command:

python Evaluation/eval-as.py -f configs/[configFile]

Similarily, we can evaluate the embeddings for other dataset by running their corresponding files in the Evaluation folder.

Plotting the Graph

To plot the graphs for MAP and MRR, run the command:

python graph.py -f configs/[configFile] -d [datasetName]

datasetName = as/sbm/otc/uci

This will generate the plot for MAP and MRR for the corresponding dataset.

Reference

If you find this code useful in your research, please cite:

@article{xu2021dyng2g,
  title={DynG2G: An Efficient Stochastic Graph Embedding Method for Temporal Graphs},
  author={Xu, Mengjia and Singh, Apoorva Vikram and Karniadakis, George Em},
  journal={arXiv preprint arXiv:2109.13441},
  year={2021}
  url={https://arxiv.org/pdf/2109.13441.pdf}
}

lwhere / DynG2G