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https://github.com/nd7141/AWE
Compute graph embeddings via Anonymous Walk Embeddings
https://github.com/nd7141/AWE
Last synced: about 2 months ago
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Compute graph embeddings via Anonymous Walk Embeddings
- Host: GitHub
- URL: https://github.com/nd7141/AWE
- Owner: nd7141
- Created: 2017-07-06T21:30:30.000Z (about 7 years ago)
- Default Branch: master
- Last Pushed: 2018-07-17T18:02:05.000Z (about 6 years ago)
- Last Synced: 2024-05-21T17:23:02.670Z (4 months ago)
- Language: Jupyter Notebook
- Homepage: https://arxiv.org/abs/1805.11921
- Size: 2.33 MB
- Stars: 81
- Watchers: 7
- Forks: 23
- Open Issues: 3
-
Metadata Files:
- Readme: Readme.md
Awesome Lists containing this project
- awesome-graph-classification - [Python Reference
README
### Intro
The code is for the paper Anonymous Walk Embeddings (https://arxiv.org/abs/1805.11921) that creates embeddings for entire graphs, based on feature-based and data-driven approaches, and evaluates the results on graph classification task.
### Tutorial
Check a notebook `Tutorial.ipynb` to see how to get network embeddings, load/save embeddings matrix, calculate kernel matrix, and perform SVM calculation.
### Running code
The following command will run data-driven model with the parameters.
```
python AnonymousWalkEmbeddings.py --dataset mutag --root ./Datasets --window_size 16 --batch_size 100 --batches_per_epoch 100 --num_samples 64 --steps 10 --results_folder mutag_results
```
The following command will run data-driven model with the parameters.
```
python AnonymousWalkKernel.py --dataset mutag --root ./Datasets --steps 10 --method sampling --MC 10000 --results_folder mutag_results
```
Alternatively, you can change default parameters in the file doc2vec.py.
Below is the list of all arguments:
- **dataset** Name of the dataset (and folders to save/load data)
- **batch_size** Number of (context, target) pairs per batch
- **window_size** Number of words in the context
- **embedding_size_w** Dimension of word embeddings
- **embedding_size_d** Dimensions of doc embeddings
- **num_samples** number of (negative) samples for every target word.
- **concat** Concatenate *or* Average embeddings of words.
- **loss_type** Sampled softmax loss *or* NCE loss
- **optimize** Adagrad *or* SGD
- **learning_rate** Learning rate
- **root** Location of dataset
- **ext** Extension of the graph files
- **steps** Number of steps in a random walk.
- **epochs** Number of global iterations.
- **batches_per_epoch** number of batches per epoch for each graph
- **candidate_func** None (loguniform by default) or uniform
- **graph_labels** None, edges, nodes, edges_nodes
- **method** Sampling or Exact.
- **MC** Number of Monte-Carlo iterations.
- **results_folder** Folder for storing results.
#### Running with Docker
To run the code on docker, we first need to obtain an appropriate image. After installing [Docker](https://docs.docker.com/engine/installation/) on your computer, you first [pull](https://docs.docker.com/docker-hub/repos/) an image `gcr.io/tensorflow/tensorflow` for Tensorflow:
```
docker pull gcr.io/tensorflow/tensorflow
```
After that you need to build a Dockerfile. You should have in the current directory Dockerfile. To avoid compression of all the files under current directory, we recommend to create a separate folder for Dockerfile and navigate there.
```
cd docker
docker build -t awe-docker .
```
You can test available images on your computer with `docker images`. Once you have `awe-docker` image you can run the code with.
```
docker run -i --user $(id -u):$(id -g) --mount type=bind,source=/home/sivanov/Datasets,target=/src/Datasets --mount type=bind,source=/home/sivanov/awe/,target=/src/awe/ --name='sergey.mutag.1' -t awe-docker:latest python /src/awe/AnonymousWalkEmbeddings.py --dataset mutag --root /src/Datasets/ --window_size 16 --batch_size 100 --batches_per_epoch 100 --num_samples 64 --results_folder /src/awe/mutag_results
```
Here, `--user $(id -u):$(id -g)` makes sure your results will be written with your user permission. `--mount type=bind,source=/home/sivanov/Datasets,target=/src/Datasets` mounts your host directory with datasets into docker image under /src/Datasets directory. `--mount type=bind,source=/home/sivanov/awe/,target=/src/awe/` mounts your host directory /home/sivanov/awe/ with scripts into an image directory /src/awe/. `--name='sergey.mutag.1'` sets the name of container. `-t awe-docker:latest` provides a name of the image.
After running, you should get results in your host machine at awe/mutag_results directory.
### Citation
If you use the code, please consider citing our work.
```
@InProceedings{pmlr-v80-ivanov18a,
title = {Anonymous Walk Embeddings},
author = {Ivanov, Sergey and Burnaev, Evgeny},
booktitle = {Proceedings of the 35th International Conference on Machine Learning},
pages = {2191--2200},
year = {2018},
editor = {Dy, Jennifer and Krause, Andreas},
volume = {80},
series = {Proceedings of Machine Learning Research},
address = {Stockholmsmässan, Stockholm Sweden},
month = {10--15 Jul},
publisher = {PMLR},
pdf = {http://proceedings.mlr.press/v80/ivanov18a/ivanov18a.pdf},
url = {http://proceedings.mlr.press/v80/ivanov18a.html}
}
```