This repo summarizes papers I've read for machine learning on graphs. I'm also writing tutorials on zhihu.com and they're in Chinese.

I use basic packages from Anaconda3 with Python 3.8.5. To make my life easier, I also use the following packages to implement models. Please see requirements.txt for the full list.

torch==1.7.0
torch_geometric==1.6.3
ogb==1.2.3
scikit-multilearn==0.2.0

The following are papers that I'll cover in this repo.

• Distributed large-scale natural graph factorization. Amr Ahmed, Nino Shervashidze, Shravan Narayanamurthy, Vanja Josifovski, and Alexander J Smola. WWW 2013.

• Grarep: Learning graph representations with global structural information. Shaosheng Cao, Wei Lu, and Qiongkai Xu. CIKM 2015.

• Asymmetric transitivity preserving graph embedding. Mingdong Ou, Peng Cui, Jian Pei, Ziwei Zhang, and Wenwu Zhu. KDD 2016.

• Deepwalk: Online learning of social representations. Bryan Perozzi, Rami Al-Rfou, and Steven Skiena. KDD 2014.

• node2vec: Scalable feature learning for networks. Aditya Grover and Jure Leskovec. KDD 2014.

• struc2vec: Learning node representations from structural identity. Leonardo FR Ribeiro, Pedro HP Saverese, and Daniel R Figueiredo. KDD 2017.