From Conda

GraphVite can be installed through conda with only one line.

conda install -c milagraph graphvite cudatoolkit=$(nvcc -V | grep -Po "(?<=V)\d+\.\d+")

By default, this will install all the dependencies. If you only need embedding training without evaluation, you can use the following alternative.

conda install -c milagraph graphvite-mini cudatoolkit=$(nvcc -V | grep -Po "(?<=V)\d+\.\d+")

From Source

To install GraphVite from source, you need to have conda installed.

git clone
cd graphvite
conda install -y --file conda/requirements.txt
mkdir build
cd build && cmake .. && make && cd -
cd python && python install && cd -

Quick Start

Once installed, we can run the following quick-start example of the node embedding application.

graphvite baseline quick start

The example will automatically download a social network dataset called BlogCatalog, where nodes correspond to blog users. For each node, we learn an embedding vector that preserves its neighborhood structure. The learned embeddings are evaluated on link prediction and node classification tasks.

Typically, the example takes no more than 1 minute. We 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

Another interesting example is a synthetic math dataset of arithmetic operations. Check out this knowledge graph example with

graphvite baseline math