Vertex Reinforced Random Walk for Network Embedding (reinforce2vec)

This repository provides a reference implementation of reinforce2vec as described in the paper in SDM 2020:

Vertex-reinforced Random Walk for Network Embedding.
Wenyi Xiao, Huan Zhao, Vincent W. Zheng, Yangqiu Song.
https://arxiv.org/abs/2002.04497

We propose to use non-Markovian random walk, variants of vertex-reinforced random walk (VRRW), to fully use the history of a random walk path. To solve the getting stuck problem of VRRW, we introduce an exploitation-exploration mechanism to help the random walk jump out of the stuck set. reinforce2vec consists of two random walk models: VRRW and DRRW.

Basic Usage

Node Classification

To run drrw on PPI, you can use the following command:
python train.py --task unsupervised_node_classification --dataset ppi --model drrw --explore exploration

Link Prediction

To run drrw on Facebook, you can use the following command:
python link_prediction_my.py --model vrrw --input graphs/facebook.edgelist --explore exploration

Citing

If you find reinforce2vec useful for your research, please consider citing the following paper:

@article{xiao2020vertex,
  title={Vertex-reinforced Random Walk for Network Embedding},
  author={Xiao, Wenyi and Zhao, Huan and Zheng, Vincent W and Song, Yangqiu},
  journal={arXiv preprint arXiv:2002.04497},
  year={2020}
}

Miscellaneous

Please send any questions you might have about the code and/or the algorithm to wxiaoae@cse.ust.hk.