Graph Neural Networks with Feature-wise Linear Modulation (GNN-FiLM) Paper link: https://arxiv.org/abs/1906.12192 Author's code repo: [https://github.com/ Microsoft/tf-gnn-samples](https://github.com/ Microsoft/tf-gnn-samples) Dataset Statics Dataset #Graphs # Nodes # Edges # Classes PPI 20 44906 1226368 121 Refer to Planetoid. Results TL_BACKEND="tensorflow" python film_trainer.py --dataset ppi --lr 0.001 --l2_coef 5e-4 --drop_rate 0.1 --hidden_dim 160 --num_layers 4 TL_BACKEND="torch" python film_trainer.py --dataset ppi --lr 0.001 --l2_coef 5e-4 --drop_rate 0.1 --hidden_dim 160 --num_layers 4 TL_BACKEND="paddle" python film_trainer.py --dataset ppi --lr 0.001 --l2_coef 5e-4 --drop_rate 0.1 --hidden_dim 160 --num_layers 4 Dataset Paper Our(tf) Our(torch) Our(pd) PPI 84.1±0.5 70±0.7 94±0.7 70±0.7