Here is a quick-start example that illustrate the pipeline in GraphVite. If you don’t
pytorch installed, simply add
--no-eval to skip the evaluation stage.
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, and evaluate the embeddings by using them as features for multi-label node classifcation.
Typically, the example takes no more than 1 minute. You will obtain some output like
Batch id: 6000 loss = 0.371641 macro-F1@20%: 0.236794 micro-F1@20%: 0.388110
Note that the F1 scores may vary across different trials, as only one random split is evaluated for quick demonstration here.
The learned embeddings are saved into a compressed numpy dump. You can load them for further use
>>> import pickle >>> with open("line_blogcatalog.pkl", "rb") as fin: >>> blogcatalog = pickle.load(fin) >>> names = blogcatalog.id2name >>> embeddings = blogcatalog.vertex_embeddings >>> print(names, embddings)
As the embeddings might be further used in other downstream tasks, it would be helpful if they can be obtained in the easiest way.
For a more in-depth tutorial about GraphVite, take a look at