HEAT: Hyperbolic Embedding of Attributed Networks

Reference implementation of algorithm described in https://link.springer.com/chapter/10.1007/978-3-030-62362-3_4

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

Requirements:

Python3
Numpy
Scipy
Scikit-learn
Scikit-multilearn
Keras

Required packages (pip)

Install the required packages with

pip install -r requirements.txt

Setup environment (conda)

The conda environment is described in heat_env.yml.

Run

conda env create -f heat_env.yml

to create the environment, and

conda activate heat-env

to activate it.

How to use:

Run the code with

python main.py --edgelist path/to/edgelist.tsv --features path/to/features.csv -e *num_epochs* --embedding path/to/save/embedding --alpha *alpha_value*

Additional options can be viewed with

python main.py --help

Input Data Format

Graph

Graphs are given as tab-separated edgelists in the form:

u\tv\tw

where w is the weight of the connection between nodes u and v
u and v must be integers
every int in [0, N-1] must appear in the edgelist

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

@inproceedings{mcdonald2020heat,
  title={HEAT: Hyperbolic Embedding of Attributed Networks},
  author={McDonald, David and He, Shan},
  booktitle={International Conference on Intelligent Data Engineering and Automated Learning},
  pages={28--40},
  year={2020},
  organization={Springer}
}