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Attention-based Graph Neural Network(AGNN)

Dataset Statics

Refer to Planetoid.

Results

TL_BACKEND=tensorflow python agnn_trainer.py --dataset cora --lr 0.01 --l2_coef 0.002 --drop_rate 0.7
TL_BACKEND=tensorflow python agnn_trainer.py --dataset citeseer --lr 0.005 --l2_coef 0.006 --drop_rate 0.5
TL_BACKEND=tensorflow python agnn_trainer.py --dataset pubmed --n_att_layers 4 --lr 0.008 --l2_coef 0.005 --drop_rate 0.5
TL_BACKEND=torch python agnn_trainer.py --dataset cora --lr 0.01 --l2_coef 0.005 --drop_rate 0.7
TL_BACKEND=torch python agnn_trainer.py --dataset citeseer --lr 0.001 --l2_coef 0.02 --drop_rate 0.5
TL_BACKEND=torch python agnn_trainer.py --dataset pubmed --n_att_layers 4 --lr 0.01 --l2_coef 0.002 --drop_rate 0.7
TL_BACKEND=paddle python agnn_trainer.py --dataset cora --lr 0.02 --l2_coef 0.001 --drop_rate 0.5
TL_BACKEND=paddle python agnn_trainer.py --dataset citeseer --lr 0.005 --l2_coef 0.01 --drop_rate 0.7
TL_BACKEND=paddle python agnn_trainer.py --dataset pubmed --n_att_layers 4 --lr 0.02 --l2_coef 0.002 --drop_rate 0.6
Dataset Paper Our(tf) Our(pd) Our(torch)
cora 83.1 83.28(±0.64) 83.48(±0.35) 83.0(±0.65)
citeseer 71.7 71.7(±0.53) 73.24(±0.71) 72.52(±1.13)
pubmed 79.9 79.02(±0.82) 78.94(±0.43) 79.10(±0.20)