shenweichen/GraphEmbedding

Health v2 · maintenance only

Flags: no_releases

Adoption not part of the score

3845 stars · 995 forks observed · 2026-08-28

What it is AI-extracted, prompt v1, taxonomy v1, 2026-08-29, confidence not recorded

A Python library providing implementations of classic graph embedding algorithms including DeepWalk, LINE, Node2Vec, SDNE, and Struc2Vec. It includes runnable example scripts for learning node representations from network data.

Use cases

• generate node embeddings for a graph
• run deepwalk on a network
• learn node2vec representations
• compare graph embedding algorithms
• embed social network nodes for downstream ML
• learn structural node representations with struc2vec

When to choose

• you need ready-to-use implementations of classic graph embedding algorithms like DeepWalk, LINE, Node2Vec, SDNE, or Struc2Vec
• you want reference implementations with example scripts for learning and experimentation
• you are benchmarking or studying network embedding methods in Python

When to avoid

• you need large-scale, production-grade distributed graph embedding
• you need modern GNN-based methods like GraphSAGE or GAT
• you need a maintained library with broad ecosystem support outside Python/TensorFlow

Facets

library · maturity active

machine-learning deep-learning data-science machine-learning data-science graph-processing python graph-embedding deepwalk node2vec line sdne struc2vec network-embedding representation-learning algorithms

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markdown · JSON · MCP: product_card(name="shenweichen/GraphEmbedding")