HEADNet: Hyperbolic Embedding of Attributed Directed Networks

Reference implementation of HEADNet algorithm

Authors: David MCDONALD (davemcdonald93@gmail.com) and Shan HE (s.he@cs.bham.ac.uk)

Requirements:

• Python3
• Numpy
• Scipy
• Scikit-learn
• Scikit-multilearn
• Keras

Setup environment (conda)

The conda environment is described in environment.yml.

Run

conda env create -f headnet_env.yml

to create the environment, and

conda activate headnet-env

to activate it.

How to use:

Run the code with

python main.py --graph path/to/graph.npz --features path/to/features.csv --embedding path/to/save/embedding.csv -e *num_epochs* --dim *embedding_dim*

Additional options can be viewed with

python main.py --help

Input Data Format

Graph

Graphs are given as sparse adjacency matrices

Node attributes and labels

labels and features should be comma separated tables indexed by node id

Citation:

If you find this useful, please use the following citation (under review)

@article{mcdonald2020headnet,
  title={HEADNet: Hyperbolic Embedding of Attributed Directed Networks},
  author={McDonald, David and He, Shan},
  journal={IEEE Transactions on Neural Networks and Learning Systems},
  year={2020},
  publisher={IEEE}
}