shenweichen/GraphEmbedding
Python library implementing graph embedding algorithms for learning low-dimensional vector representations of graph nodes.
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This repository provides implementations of classical graph embedding algorithms including DeepWalk, LINE, node2vec, SDNE, and struc2vec. These algorithms learn low-dimensional vector representations of nodes in a graph by preserving structural or proximity information, similar to how Word2vec learns word embeddings from textual corpora.
Frequently asked
- What is shenweichen/GraphEmbedding?
- Python library implementing graph embedding algorithms for learning low-dimensional vector representations of graph nodes.
- Is GraphEmbedding open source?
- Yes — shenweichen/GraphEmbedding is open source, released under the MIT license.
- What language is GraphEmbedding written in?
- shenweichen/GraphEmbedding is primarily written in Python.
- How popular is GraphEmbedding?
- shenweichen/GraphEmbedding has 3.8k stars on GitHub.
- Where can I find GraphEmbedding?
- shenweichen/GraphEmbedding is on GitHub at https://github.com/shenweichen/GraphEmbedding.