What it does OpenNE is a Python toolkit that trains and tests network-embedding models—algorithms that turn graph nodes into dense vectors. It wraps ten methods (DeepWalk, LINE, node2vec, GCN, TADW, and others) behind one standardized CLI and Python API. Feed it an edgelist or adjlist, pick a model, and it spits out node vectors plus optional classification scores.

The interesting bit The real value is the unification, not novelty. Every model speaks the same input/output format, and the TensorFlow implementations can run on GPUs. The authors also include TADW, their own method that folds in text node attributes—handy when your graph nodes come with baggage.

Key highlights

• Ten models in one package: DeepWalk, LINE, node2vec, GraRep, GCN, HOPE, GF, SDNE, LE, and TADW
• Single CLI entry point (python -m openne --method ...) with model-specific flags
• GPU training via TensorFlow for the implemented models
• Built-in node-classification evaluation using a train/test split (default 50/50)
• Reproduces published results on Wiki and Cora benchmarks; includes visualization examples with t-SNE/PCA

Caveats

• Installation is manual: clone, pip install -r requirements.txt, then cd src && python setup.py install
• Some original implementations are faster (e.g., original DeepWalk 52s vs. OpenNE 42s on Wiki—actually competitive, but LINE lags at 90s vs. 70s)
• README notes the original Wiki dataset link is dead; they host a mirror

Verdict Grab this if you need to benchmark multiple embedding algorithms without maintaining ten codebases. Skip it if you want a batteries-included pip install or if you only need one modern method like GraphSAGE—this is a 2017-era snapshot of the field.