Project AURIONAI Use-Cases Research & Innovation
Stanford University
graphfamily

node2vec

Scalable method for learning node embeddings from biased random walks over a network. Paper by Grover and Leskovec (2016).

Stanford UniversityStanford UniversityUSA
Figure from the node2vec paper: BFS and DFS search strategies from node uu (k=3k=3).
Levelfamily
Classgraph
Parametersnot in the notes
Contextnot in the notes
Released3 Jul 2016
Opennessopen source
Runs onnot in the notes
Statusactive

R&D Topics

Graph neural networks and graph transformersproduced by

Identity

Developer
Stanford University
Family
not in the notes
Released
3 Jul 2016 sourcesannouncement page
Weights published
–
Record checked
12 Oct 2026
Where this comes from
Developer and dates from the research notes. Release date is the vendor's public announcement; weights date is when open weights were published.

Architecture and Size

Architecture
other Biased random walks plus skip-gram node embeddings
Total parameters
– sources
Active parameters
–
Checkpoint parameters
–
Context length
not in the notes
Input
graph
Output
graph
Where this comes from
Parameter counts as the developer states them. Where the checkpoint holds a different total, both are shown (register A16).

Openness and Access

Openness
open source
Licence
MIT License sources
Access routes
not in the notes
Where this comes from
The licence file governs over registry tags and aggregator labels.

Deployment

Runs on
not in the notes
Footprint
Licence is that of the authors' aditya-grover/node2vec repository (MIT).
Runtimes
not in the notes
Where this comes from
Deployment tiers and runtimes as named in the notes. Footprint is the notes' figure, not a measurement by AURION.

Indonesia

Indonesian support
not stated
Regional languages
not in the notes
Jakarta hosting
unknown
Languages
not in the notes
Where this comes from
Indonesia fields are carried on every model; unknown is an allowed value.

Benchmark Results

The notes record no benchmark result for this model.

Lineage

The notes record no lineage for this model.

Industry Use Cases

No industry use case links this model.

Sources

  1. arxiv.org/abs/1607.00653 https://arxiv.org/abs/1607.00653
  2. github.com/aditya-grover/node2vec https://github.com/aditya-grover/node2vec
  3. GitHub - pyg-team/pytorch_geometric: Graph Neural Network Library for PyTorch github.com https://github.com/pyg-team/pytorch_geometric
  4. pytorch-geometric.readthedocs.io/en/latest/modules/nn.html https://pytorch-geometric.readthedocs.io/en/latest/modules/nn.html
  5. huggingface.co/datasets/pwc-archive/methods https://huggingface.co/datasets/pwc-archive/methods
  6. arxiv.org/html/1607.00653 https://arxiv.org/html/1607.00653

To cite this page: Project AURION, AI Use-Cases Research & Innovation, "node2vec", https://aurion.semai.id/models/node2vec/, records of October 2026.