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GraphEmbedding is an open-source Python project for implementing and experimenting with graph embedding algorithms. It follows a simple “graph in, embedding out” design. The library uses NetworkX graphs as input and produces vector representations for graph nodes. It includes implementations of DeepWalk, LINE, Node2Vec, SDNE, and Struc2Vec. Users can configure walks, embedding dimensions, training windows, epochs, and other model-specific parameters. Example scripts demonstrate how to train models and retrieve embeddings for downstream graph analysis or machine learning tasks.
Features
- DeepWalk graph embedding
- LINE network embedding
- Node2Vec feature learning
- SDNE deep network embedding
- Struc2Vec structural embeddings
- NetworkX graph input and vector export
Project Samples
Categories
AlgorithmsLicense
MIT LicenseFollow GraphEmbedding
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