Project AURIONAI Use-Cases Research & Innovation
graphfamily

GraphSAGE

Inductive method that learns node embeddings by sampling and aggregating features from neighbours, so it works on unseen nodes and large graphs. Paper by Hamilton et al. (2017).

Stanford UniversityStanford UniversityUSA
Stanford University
Levelfamily
Classgraph
Parametersnot in the notes
Contextnot in the notes
Released7 Jun 2017
Opennessunknown
Runs onnot in the notes
Statusactive

R&D Topics

Graph neural networks and graph transformersexemplar

Identity

Developer
Stanford University
Family
not in the notes
Released
7 Jun 2017 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
GNN Inductive neighbourhood sampling and aggregation
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
unknown
Licence
not in the notes 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
Reference repository williamleif/GraphSAGE has no standard licence identified by GitHub; PyTorch Geometric implementation is MIT. ONNX Model Zoo publishes SAGEConv graph models.
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/1706.02216 https://arxiv.org/abs/1706.02216
  2. github.com/williamleif/GraphSAGE https://github.com/williamleif/GraphSAGE
  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. github.com/onnx/models/tree/main/Graph_Machine_Learning/... https://github.com/onnx/models/tree/main/Graph_Machine_Learning/sageconv_Opset16_graph_convolutions
  6. huggingface.co/datasets/pwc-archive/methods https://huggingface.co/datasets/pwc-archive/methods
  7. arxiv.org/html/1706.02216 https://arxiv.org/html/1706.02216

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