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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

* Fix lint

* Fix test cases

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* rewrite sanity checks

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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

entity_classify.py

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

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

model.py

[NN] RGCN modules (#744 )

2019-08-23 16:38:48 -04:00

README.md

[Dataset] RDF dataset with DGL-Dataset template (#1869 )

2020-07-30 09:12:00 +08:00

README.md

Relational-GCN

Paper: https://arxiv.org/abs/1703.06103
Author's code for entity classification: https://github.com/tkipf/relational-gcn
Author's code for link prediction: https://github.com/MichSchli/RelationPrediction

Dependencies

Two extra python packages are needed for this example:

MXNet nightly build
requests
rdflib
pandas

pip install mxnet --pre
pip install requests rdflib pandas

Example code was tested with rdflib 4.2.2 and pandas 0.23.4

Entity Classification

AIFB: accuracy 97.22% (5 runs, DGL), 95.83% (paper)

DGLBACKEND=mxnet python3 entity_classify.py -d aifb --testing --gpu 0

MUTAG: accuracy 70.59% (5 runs, DGL), 73.23% (paper)

DGLBACKEND=mxnet python3 entity_classify.py -d mutag --l2norm 5e-4 --n-bases 40 --testing --gpu 0

BGS: accuracy 86.21% (5 runs, DGL, n-basese=20), 83.10% (paper)

DGLBACKEND=mxnet python3 entity_classify.py -d bgs --l2norm 5e-4 --n-bases 20 --testing --gpu 0