hell-to-heaven / harp

Code for the AAAI 2018 Paper "HARP: Hierarchical Representation Learning for Networks"

HARP

Code for the AAAI 2018 paper "HARP: Hierarchical Representation Learning for Networks". HARP is a meta-strategy to improve several state-of-the-art network embedding algorithms, such as DeepWalk, LINE and Node2vec.

You can read the preprint of our paper on Arxiv.

This code run with Python 2.

Installation

The following Python packages are required to install HARP.

magicgraph is a library for processing graph data. To install, run the following commands:

git clone https://github.com/phanein/magic-graph.git
cd magic-graph
python setup.py install

Then, install HARP and the other requirements:

git clone https://github.com/GTmac/HARP.git
cd HARP
pip install -r requirements.txt

Usage

To run HARP on the CiteSeer dataset using LINE as the underlying network embedding model, run the following command:

python src/harp.py --input example_graphs/citeseer/citeseer.mat --model line --output citeseer.npy --sfdp-path bin/sfdp_linux

Parameters available:

--input: input_filename

1. --format mat for a Matlab .mat file containing an adjacency matrix. By default, the variable name of the adjacency matrix is network; you can also specify it with --matfile-variable-name.

2. --format adjlist for an adjacency list, e.g:

   1 2 3 4 5 6 7 8 9 11 12 13 14 18 20 22 32

   2 1 3 4 8 14 18 20 22 31

   3 1 2 4 8 9 10 14 28 29 33

   ...

3. --format edgelist for an edge list, e.g:

   1 2

   1 3

   1 4

   2 5

   ...

--output: output_filename The output representations in Numpy .npy format. Note that we assume the nodes in your input file are indexed from 0 to N - 1.

--model model_name The underlying network embeddings model to use. Could be deepwalk, line or node2vec. Note that node2vec uses the default parameters, which is p=1.0 and q=1.0.

--sfdp-path sfdp_path Path to the binary file of SFDP, which is the module we used for graph coarsening. You can set it to sfdp_linux, sfdp_osx or sfdp_windows.exe depending on your operating system.

More options: The full list of command line options is available with python src/harp.py --help.

Evaluation

To evaluate the embeddings on a multi-label classification task, run the following command:

python src/scoring.py -e citeseer.npy -i example_graphs/citeseer/citeseer.mat -t 1 2 3 4 5 6 7 8 9

Where -e specifies the embeddings file, -i specifies the .mat file containing node labels, and -t specifies the list of training example ratios to use.

Note

SFDP is a library for multi-level graph drawing, which is a part of GraphViz. We use SFDP for graph coarsening in this implementation. Note that SFDP is included as a binary file under /bin; please choose the proper binary file according to your operation system. Currently we have the binary files under OSX, Linux and Windows.

Citation

If you find HARP useful in your research, please cite our paper:

@inproceedings{harp,
	title={HARP: Hierarchical Representation Learning for Networks},
	author={Chen, Haochen and Perozzi, Bryan and Hu, Yifan and Skiena, Steven},
	booktitle={Proceedings of the Thirty-Second AAAI Conference on Artificial Intelligence},
	year={2018},
	organization={AAAI Press}
}