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* 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 * Fix * Fix * Fix * Fix * Fix * Fix * Update * Fix test * Fix * Update * Use DGLError * Update * Update * Update * Update * Fix * Fix * Fix * Fix * Fix * Fix * Fix * Fix * Update * Fix * Update * Fix * Fix * Fix * Update * Fix * Update * Fix * Update * Update * Update * Update * Update * Update * Update * Fix * Fix * Update * Update * Update * Update * Update * Update * rewrite sanity checks * delete unnecessary checks * Update * Update * Update * Update * Update * Update * Update * Update * Fix * Update * Update * Update * Fix * Fix * Fix * Update * Fix * Update * Fix * Fix * Update * Fix * Update * Fix 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>
1010 B
1010 B
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
- Tensorflow 2.2+
- requests
- rdflib
- pandas
pip install requests tensorflow rdflib pandas
export DGLBACKEND=tensorflow
Example code was tested with rdflib 4.2.2 and pandas 0.23.4
Entity Classification
AIFB: accuracy 92.78% (5 runs, DGL), 95.83% (paper)
python3 entity_classify.py -d aifb --testing --gpu 0
MUTAG: accuracy 71.47% (5 runs, DGL), 73.23% (paper)
python3 entity_classify.py -d mutag --l2norm 5e-4 --n-bases 30 --testing --gpu 0
BGS: accuracy 93.10% (5 runs, DGL n-base=25), 83.10% (paper n-base=40)
python3 entity_classify.py -d bgs --l2norm 5e-4 --n-bases 25 --testing --gpu 0