DeepWalk

DeepWalk uses short random walks to learn representations for vertices in graphs.

Usage

Example Usage

$deepwalk --input example_graphs/karate.adjlist --output karate.embeddings

--input: input_filename

1. --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
   ...
   

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

   1 2
   1 3
   1 4
   ...
   

3. --format mat for a Matlab .mat file containing an adjacency matrix

   (note, you must also specify the variable name of the adjacency matrix --matfile-variable-name)

--output: output_filename

The output representations in skipgram format - first line is header, all other lines are node-id and d dimensional representation:

34 64
1 0.016579 -0.033659 0.342167 -0.046998 ...
2 -0.007003 0.265891 -0.351422 0.043923 ...
...

Full Command List

The full list of command line options is available with $deepwalk --help

Evaluation

Here, we will show how to evaluate DeepWalk on the BlogCatalog dataset used in the DeepWalk paper. First, we run the following command to produce its DeepWalk embeddings:

deepwalk --format mat --input example_graphs/blogcatalog.mat
--max-memory-data-size 0 --number-walks 80 --representation-size 128 --walk-length 40 --window-size 10
--workers 1 --output example_graphs/blogcatalog.embeddings

The parameters specified here are the same as in the paper. If you are using a multi-core machine, try to set --workers to a larger number for faster training. On a single machine with 24 Xeon E5-2620 @ 2.00GHz CPUs, this command takes about 20 minutes to finish (--workers is set to 20). Then, we evaluate the learned embeddings on a multi-label node classification task with example_graphs/scoring.py:

python example_graphs/scoring.py --emb example_graphs/blogcatalog.embeddings
--network example_graphs/blogcatalog.mat
--num-shuffle 10 --all

This command finishes in 8 minutes on the same machine. For faster evaluation, you can set --num-shuffle to a smaller number, but expect more fluctuation in performance. The micro F1 and macro F1 scores we get with different ratio of labeled nodes are as follows:

% Labeled Nodes	10%	20%	30%	40%	50%	60%	70%	80%	90%
Micro-F1 (%)	35.86	38.51	39.96	40.76	41.51	41.85	42.27	42.35	42.40
Macro-F1 (%)	21.08	23.98	25.71	26.73	27.68	28.28	28.88	28.70	28.21

Note that the current version of DeepWalk is based on a newer version of gensim, which may have a different implementation of the word2vec model. To completely reproduce the results in our paper, you will probably have to install an older version of gensim(version 0.10.2).

Requirements

• numpy
• scipy

(may have to be independently installed)

Installation

1. cd deepwalk
2. pip install -r requirements.txt
3. python setup.py install

Citing

If you find DeepWalk useful in your research, we ask that you cite the following paper:

@inproceedings{Perozzi:2014:DOL:2623330.2623732,
 author = {Perozzi, Bryan and Al-Rfou, Rami and Skiena, Steven},
 title = {DeepWalk: Online Learning of Social Representations},
 booktitle = {Proceedings of the 20th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining},
 series = {KDD '14},
 year = {2014},
 isbn = {978-1-4503-2956-9},
 location = {New York, New York, USA},
 pages = {701--710},
 numpages = {10},
 url = {http://doi.acm.org/10.1145/2623330.2623732},
 doi = {10.1145/2623330.2623732},
 acmid = {2623732},
 publisher = {ACM},
 address = {New York, NY, USA},
 keywords = {deep learning, latent representations, learning with partial labels, network classification, online learning, social networks},
}

Misc

DeepWalk - Online learning of social representations.

• Free software: GPLv3 license
• Documentation: http://deepwalk.readthedocs.org.