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Usage of MoG

Introduce

Under each folder:

• gnn.py and conv.py are basic gnn(GCN,SAGE,PNA,Deepergcn...) implementation codes

• Splearner.py implements a node-granular graph pruner

• MoE.py implements a model that mixes pruners with different sparsities

• MoG.py bridges the connection between MoE and GNN

Usage example

For the ogbn_proteins dataset, please cd ogbn_proteins first.

python main.py --use_gpu --conv_encode_edge --use_one_hot_encoding --num_layers 28 --block res+ --gcn_aggr max --k_list 1 --expert_select 1
python main.py --use_gpu --conv_encode_edge --use_one_hot_encoding --num_layers 28 --block res+ --gcn_aggr max --k_list 0.9 0.7 0.5 --expert_select 2
python main.py --use_gpu --conv_encode_edge --use_one_hot_encoding --num_layers 28 --block res+ --gcn_aggr max --k_list 0.7 0.5 0.3 --expert_select 2
python main.py --use_gpu --conv_encode_edge --use_one_hot_encoding --num_layers 28 --block res+ --gcn_aggr max --k_list 0.4 0.3 0.2 --expert_select 2

For the MNIST dataset, please cd MNIST first.

python main.py --k_list 1 --expert_select 1
python main.py --k_list 0.8 0.5 0.4 --expert_select 2
python main.py --k_list 0.6 0.3 0.2 --expert_select 2
python main.py --k_list 0.35 0.1 0.1 --expert_select 2