Docs | Tutorials | Benchmarks | Pre-trained models
GraphVite is a general graph embedding engine, dedicated to high-speed and large-scale embedding learning in various applications.
GraphVite provides complete training and evaluation pipelines for 3 applications: node embedding, knowledge graph embedding and graph & high-dimensional data visualization. Besides, it also includes 9 popular models, along with their benchmarks on a bunch of standard datasets.
Node Embedding | Knowledge Graph Embedding | Graph & High-dimensional Data Visualization |
---|---|---|
![]() |
![]() |
![]() |
Here is a summary of the training time of GraphVite along with the best open-source implementations on 3 applications. All the time is reported based on a server with 24 CPU threads and 4 V100 GPUs.
Training time of node embedding on Youtube dataset.
Model | Existing Implementation | GraphVite | Speedup |
---|---|---|---|
DeepWalk | 1.64 hrs (CPU parallel) | 1.19 mins | 82.9x |
LINE | 1.39 hrs (CPU parallel) | 1.17 mins | 71.4x |
node2vec | 24.4 hrs (CPU parallel) | 4.39 mins | 334x |
Training / evaluation time of knowledge graph embedding on FB15k dataset.
Model | Existing Implementation | GraphVite | Speedup |
---|---|---|---|
TransE | 1.31 hrs / 1.75 mins (1 GPU) | 13.5 mins / 54.3 s | 5.82x / 1.93x |
RotatE | 3.69 hrs / 4.19 mins (1 GPU) | 28.1 mins / 55.8 s | 7.88x / 4.50x |
Training time of high-dimensional data visualization on MNIST dataset.
Model | Existing Implementation | GraphVite | Speedup |
---|---|---|---|
LargeVis | 15.3 mins (CPU parallel) | 13.9 s | 66.8x |
Generally, GraphVite works on any Linux distribution with CUDA >= 9.2.
The library is compatible with Python 2.7 and 3.6/3.7.
conda install -c milagraph graphvite cudatoolkit=$(nvcc -V | grep -Po "(?<=V)\d+.\d+")
If you only need embedding training without evaluation, you can use the following alternative with minimal dependencies.
conda install -c milagraph graphvite-mini cudatoolkit=$(nvcc -V | grep -Po "(?<=V)\d+.\d+")
Before installation, make sure you have conda
installed.
git clone https://github.com/DeepGraphLearning/graphvite
cd graphvite
conda install -y --file conda/requirements.txt
mkdir build
cd build && cmake .. && make && cd -
cd python && python setup.py install && cd -
!wget -c https://repo.anaconda.com/miniconda/Miniconda3-latest-Linux-x86_64.sh
!chmod +x Miniconda3-latest-Linux-x86_64.sh
!./Miniconda3-latest-Linux-x86_64.sh -b -p /usr/local -f
!conda install -y -c milagraph -c conda-forge graphvite \
python=3.6 cudatoolkit=$(nvcc -V | grep -Po "(?<=V)\d+\.\d+")
!conda install -y wurlitzer ipykernel
import site
site.addsitedir("/usr/local/lib/python3.6/site-packages")
%reload_ext wurlitzer
Here is a quick-start example of the node embedding application.
graphvite baseline quick start
Typically, the example takes no more than 1 minute. You will obtain some output like
Batch id: 6000
loss = 0.371041
------------- link prediction --------------
AUC: 0.899933
----------- node classification ------------
macro-F1@20%: 0.242114
micro-F1@20%: 0.391342
To reproduce a baseline benchmark, you only need to specify the keywords of the experiment. e.g. model and dataset.
graphvite baseline [keyword ...] [--no-eval] [--gpu n] [--cpu m] [--epoch e]
You may also set the number of GPUs and the number of CPUs per GPU.
Use graphvite list
to get a list of available baselines.
Create a yaml configuration scaffold for graph, knowledge graph, visualization or word graph.
graphvite new [application ...] [--file f]
Fill some necessary entries in the configuration following the instructions. You can run the configuration by
graphvite run [config] [--no-eval] [--gpu n] [--cpu m] [--epoch e]
You can visualize your high-dimensional vectors with a simple command line in GraphVite.
graphvite visualize [file] [--label label_file] [--save save_file] [--perplexity n] [--3d]
The file can be either a numpy dump *.npy
or a text matrix *.txt
. For the save
file, we recommend to use png
format, while pdf
is also supported.
We welcome all contributions from bug fixs to new features. Please let us know if you have any suggestion to our library.
GraphVite is developed by MilaGraph, led by Prof. Jian Tang.
Authors of this project are Zhaocheng Zhu, Shizhen Xu, Meng Qu and Jian Tang. Contributors include Kunpeng Wang and Zhijian Duan.
If you find GraphVite useful for your research or development, please cite the following paper.
@inproceedings{zhu2019graphvite,
title={GraphVite: A High-Performance CPU-GPU Hybrid System for Node Embedding},
author={Zhu, Zhaocheng and Xu, Shizhen and Qu, Meng and Tang, Jian},
booktitle={The World Wide Web Conference},
pages={2494--2504},
year={2019},
organization={ACM}
}
We would like to thank Compute Canada for supporting GPU servers. We specially thank Wenbin Hou for useful discussions on C++ and GPU programming techniques.