DeepWalk uses short random walks to learn representations for vertices in graphs.
- Example Usage
$deepwalk --input example_graphs/karate.adjlist --output karate.embeddings
--input: input_filename
--format adjlistfor 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 ...
--format edgelistfor an edge list, e.g:1 2 1 3 1 4 ...
--format matfor 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
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).
- numpy
- scipy
(may have to be independently installed)
- cd deepwalk
- pip install -r requirements.txt
- python setup.py install
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},
}
DeepWalk - Online learning of social representations.
- Free software: GPLv3 license
- Documentation: http://deepwalk.readthedocs.org.
