node2vec
No estimate
No hardware requirements for this model
The weights for this model have not been published, so it cannot be downloaded or run on your own hardware at any size. It is reachable only through its provider, and no graphics card changes that.
On record
Full specification
Everything on record for this model. Most of it describes how it was trained rather than how it runs — useful context for judging how much work went into it, and how it compares with models built at a different scale.
Origin
Who built this model, where, and when it was published.
- Organisation
- Stanford University
- Organisation type
- Academia
- Country
- United States of America
- Published
- 3 July 2016
- Authors
- Aditya Grover, Jure Leskovec
What it does
The problem areas the model was built for. A model can carry several of each.
- Domain
- Mathematics
- Task
- Pattern classification
Size
How large the model is and how much data it was trained on. Parameters are the figure that decides whether it fits on a given graphics card.
- Parameters
- 1.3M
- Training data
- tokens
If a graph has 10,000 nodes and the embeddings are 128-dimensional, then the total number of parameters would be: P=10,000×128=1,280,000
Availability
Whether you can obtain the model and run it on your own hardware, which is what decides if any of the graphics-card figures on this page apply.
- Training code
- Open source
https://github.com/aditya-grover/node2vec MIT license
How it is classified
Labels the source dataset applies when tracking notable models, and how confident it is in the entry.
- Record confidence
- Speculative
Sources
Where this record came from and when it was last checked.
- Reference
- node2vec: Scalable Feature Learning for Networks
- Last updated
- 28 November 2025
What the numbers mean
Background
node2vec was published by Stanford University, in United States of America, in July 2016. It comes out of academia.
It works in Mathematics, and is recorded as doing pattern classification.
Its weights were never published, so it can only be reached through its provider. No graphics card changes that.
Answers
node2vec — common questions
Who created node2vec?
node2vec was published by Stanford University, based in United States of America, categorised as academia.
When was node2vec released?
node2vec was published in July 2016. Capability per parameter has improved considerably since, so a newer model of the same size is often the better use of the same hardware.
What is node2vec used for?
node2vec works in Mathematics, and is recorded as handling pattern classification. These are the areas it was designed around; they describe intent rather than a hard boundary.
What GPU do I need to run node2vec?
None. node2vec is a closed model — its weights were never published, so it cannot be downloaded or run on your own hardware at any price. It is reachable only through its provider.
Is node2vec open source?
The licensing for node2vec was never recorded in our source data. We treat unstated licensing as closed, because an unrecorded licence is not one to rely on.
How many parameters does node2vec have?
node2vec has 1.3M parameters. If a graph has 10,000 nodes and the embeddings are 128-dimensional, then the total number of parameters would be: P=10,000×128=1,280,000. That figure is the total, and it is what decides how much memory the model needs — roughly half a gigabyte per billion at the compression most people use.
The other direction
Looking at it from the other side?
This page starts from the model. If you already own a card and want to know everything it will run, start from the hardware instead.