DualHGCN

This is the source code for The Web Conference (formerly WWW) 2021 paper "Multiplex Bipartite Network Embedding using Dual Hypergraph Convolutional Networks".

Overview

The unsupervised Dual HyperGraph Convolutional Network (DualHGCN) model that scalably transforms the multiplex bipartite network into two sets of homogeneous hypergraphs (left figure) and uses spectral hypergraph convolutional operators, along with intra- and inter-message passing strategies to promote information exchange within and across domains, to learn effective node embeddings (right figure).

Requirement

Python 3.6
networkx == 1.11
numpy == 1.18
sklearn == 0.22
pytorch == 1.3.1

Example Usage

To reproduce the experiments on DTI dataset, simply run:

python3 train.py

Reference

All readers are welcome to star/fork this repository and use it to reproduce our experiments or train your own data. Please kindly cite our paper:

@inproceedings{Xue2021DualHGCN,
  title     = {Multiplex Bipartite Network Embedding using Dual Hypergraph Convolutional Networks},
  author    = {Xue, Hansheng and Yang, Luwei and Rajan, Vaibhav and Jiang, Wen and Wei, Yi and Lin, Yu},
  booktitle = {WWW},
  year      = {2021}
}