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DeepWalk - Deep Learning for Graphs
| Date | Stars |
|---|---|
| 2026-07-31 | 2758 |
| 2026-08-03 | 2758 |
| 2026-08-06 | 2759 |
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===============================
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)
or `pip install -r requirements.txt` to install all dependencies
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 Excerpt of 5,000 characters
Read on GitHubWould you bet a product on this? Bounded 0–100 and slow moving.
matched fp:4023e4789b9d58d3, llm:Repository title/description: "DeepWalk - Deep Learning for Graphs" (graph representation learning algorithm).
matched fp:4023e4789b9d58d3, llm:Repository title/description: "DeepWalk - Deep Learning for Graphs" (graph representation learning algorithm).
