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MGNNI: Multiscale Graph Neural Networks with Implicit Layers (NGNNI)

Dataset Statics

Dataset # Nodes # Edges # Classes
Cornell 183 280 5
Texas 183 295 5
Wisconsin 251 466 5

Refer to WebKB.

Results

# available dataset: "cora", "citeseer", "pubmed"
TL_BACKEND="paddle" python mgnni_trainner.py --dataset cornell --lr 0.5 --l2_coef 5e-6 --model MGNNI_m_att --ks [1,2] --epochs 300
TL_BACKEND="paddle" python mgnni_trainner.py --dataset texas --lr 0.5 --l2_coef 5e-6 --model MGNNI_m_att --ks [1,2] --epochs 300
TL_BACKEND="paddle" python mgnni_trainner.py --dataset wisconsin --lr 0.5 --l2_coef 5e-6 --model MGNNI_m_att --ks [1,2] --epochs 300
TL_BACKEND="tensorflow" python mgnni_trainner.py --dataset cornell --lr 0.5 --l2_coef 5e-6 --model MGNNI_m_att --ks [1,2] --epochs 300
TL_BACKEND="tensorflow" python mgnni_trainner.py --dataset texas --lr 0.5 --l2_coef 5e-6 --model MGNNI_m_att --ks [1,2] --epochs 300
TL_BACKEND="tensorflow" python mgnni_trainner.py --dataset wisconsin --lr 0.5 --l2_coef 5e-6 --model MGNNI_m_att --ks [1,2] --epochs 300
TL_BACKEND="torch" python mgnni_trainner.py --dataset cornell --lr 0.5 --l2_coef 5e-6 --model MGNNI_m_att --ks [1,2] --epochs 300
TL_BACKEND="torch" python mgnni_trainner.py --dataset texas --lr 0.5 --l2_coef 5e-6 --model MGNNI_m_att --ks [1,2] --epochs 300
TL_BACKEND="torch" python mgnni_trainner.py --dataset wisconsin --lr 0.5 --l2_coef 5e-6 --model MGNNI_m_att --ks [1,2] --epochs 300
Dataset Paper Our(pd) Our(tf) Our(th) Our(ms)
Cornell 78.38 78.92±1.21 78.38±0.00 78.38±0.00
Texas 81.08 85.95±1.21 83.78±0.00 84.86±1.48
Wisconsin 82.35 83.53±1.07 82.35±0.00 83.92±0.88