Weisfeiler-Lehman Neural Machine for Link Prediction

This repository provides Python implementation for method proposed in the paper "M. Zhang, Y. Chen, Weisfeiler-Lehman Neural Machine for Link Prediction, KDD 2017".

Introduction

Weisfeiler-Lehman Neural Machine (WLNM) is a subgraph-based link prediction method leveraging deep learning to automatically learn graph structure features for link prediction from links' enclosing subgraphs.
The method was proposed in "M. Zhang, Y. Chen, Weisfeiler-Lehman Neural Machine for Link Prediction, KDD 2017".

Reference

1. M. Zhang and Y. Chen, Weisfeiler-Lehman Neural Machine for Link Prediction, Proc. ACM SIGKDD Conference on Knowledge Discovery and Data Mining (KDD-17).
2. Code for "M. Zhang, Y. Chen, Weisfeiler-Lehman Neural Machine for Link Prediction, KDD 2017": https://github.com/muhanzhang/LinkPrediction