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https://github.com/GTmac/HARP

Code for the AAAI 2018 Paper "HARP: Hierarchical Representation Learning for Networks"
https://github.com/GTmac/HARP

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

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# 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](https://arxiv.org/abs/1706.07845).



This code run with Python 2.



# Installation



The following Python packages are required to install HARP.



[magicgraph](https://github.com/phanein/magic-graph) 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](http://www.graphviz.org).

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}

	}