benedekrozemberczki/graph2vec
A library that learns fixed-length vector embeddings for entire graphs using an unsupervised neural approach.
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Graph2Vec implements a neural embedding framework that learns task-agnostic distributed representations of arbitrarily sized graphs. It uses a subsampling and rooted subgraph approach inspired by document embedding techniques to capture graph structure. The learned embeddings can be used for downstream tasks including graph classification, clustering, and as features for supervised learning pipelines.
Frequently asked
- What is benedekrozemberczki/graph2vec?
- A library that learns fixed-length vector embeddings for entire graphs using an unsupervised neural approach.
- Is graph2vec open source?
- Yes — benedekrozemberczki/graph2vec is open source, released under the GPL-3.0 license.
- What language is graph2vec written in?
- benedekrozemberczki/graph2vec is primarily written in Python.
- How popular is graph2vec?
- benedekrozemberczki/graph2vec has 935 stars on GitHub.
- Where can I find graph2vec?
- benedekrozemberczki/graph2vec is on GitHub at https://github.com/benedekrozemberczki/graph2vec.