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Mufei Li be444e52d9 [Doc/Feature] Refactor, doc update and behavior fix for graphs (#1983 )

* Update graph

* Fix for dgl.graph

* from_scipy

* Replace canonical_etypes with relations

* from_networkx

* Update for hetero_from_relations

* Roll back the change of canonical_etypes to relations

* heterograph

* bipartite

* Update doc

* Fix lint

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* Fix test cases

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Co-authored-by: xiang song(charlie.song) <classicxsong@gmail.com>
Co-authored-by: Minjie Wang <wmjlyjemaine@gmail.com>
Co-authored-by: Quan Gan <coin2028@hotmail.com>

2020-08-18 04:26:29 +08:00

.gitignore

[NN] nn modules & examples update (#890 )

2019-11-03 17:29:42 -05:00

coarsening.py

[NN] nn modules & examples update (#890 )

2019-11-03 17:29:42 -05:00

coordinate.py

[NN] nn modules & examples update (#890 )

2019-11-03 17:29:42 -05:00

grid_graph.py

[NN] nn modules & examples update (#890 )

2019-11-03 17:29:42 -05:00

mnist.py

[Doc/Feature] Refactor, doc update and behavior fix for graphs (#1983 )

2020-08-18 04:26:29 +08:00

README.md

[NN] nn modules & examples update (#890 )

2019-11-03 17:29:42 -05:00

README.md

Geometric Deep Learning models

This example shows how to use geometric deep learning models defined in dgl.nn.pytorch.conv for graph classification.

Currently we support following models:

Image Classification on MNIST

By transforming images to graphs, graph classifcation algorithms could be applied to image classification problems.

Usage

python mnist.py --model cheb --gpu 0
python mnist.py --model monet --gpu 0

Acknowledgement

We thank Xavier Bresson for providing code for graph coarsening algorithm and grid graph building in
CE7454_2019 Labs.