Statements (47)
| Predicate | Object |
|---|---|
| gptkbp:instance_of |
gptkb:Library
|
| gptkbp:developed_by |
Py Torch community
|
| gptkbp:enables |
predictive modeling on graphs
|
| gptkbp:features |
time-aware learning
|
| gptkbp:has |
tutorials
user guides active community performance benchmarks |
| gptkbp:has_documentation |
available online
|
| https://www.w3.org/2000/01/rdf-schema#label |
torch-geometric-temporal
|
| gptkbp:includes |
temporal message passing
|
| gptkbp:integrates_with |
other Py Torch libraries
|
| gptkbp:is |
flexible
open-source scalable efficient widely used actively maintained used in industry designed for developers used in academia designed for researchers designed for practitioners part of Py Torch Geometric ecosystem |
| gptkbp:is_compatible_with |
gptkb:Py_Torch
|
| gptkbp:is_used_for |
temporal graph deep learning
|
| gptkbp:language |
gptkb:Library
|
| gptkbp:latest_version |
latest release 0.1.0
|
| gptkbp:license |
MIT License
|
| gptkbp:offers |
graph convolutional networks
|
| gptkbp:provides |
custom loss functions
graph neural network layers spatio-temporal data handling temporal graph datasets |
| gptkbp:repository |
gptkb:archive
|
| gptkbp:requires |
gptkb:lighthouse
|
| gptkbp:supports |
dynamic graphs
link prediction graph classification node classification graph attention networks graph recurrent networks multi-relational graphs temporal graph embeddings |
| gptkbp:used_in |
research papers
|
| gptkbp:bfsParent |
gptkb:torch-geometric
|
| gptkbp:bfsLayer |
6
|