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Types & classes293 in github.com/THUDM/HGB

⨍ Functions 2,023◇ Types & classes 293
↓ 14 callersClassGCNConv
r"""The graph convolutional operator from the `"Semi-supervised Classfication with Graph Convolutional Networks" <https://arxiv.org/abs/1609.0
NC/GTN/gcn.py:10
↓ 14 callersClassdata_loader
NC/benchmark/methods/HGT/data_loader.py:7
↓ 14 callersClassdata_loader
LP/benchmark/methods/HGT/data_loader.py:10
↓ 12 callersClassindex_generator
NC/benchmark/methods/MAGNN/utils/tools.py:212
↓ 8 callersClassdata_loader
NC/benchmark/methods/HetSANN/HetSANN_MRV/scripts/data_loader.py:7
↓ 7 callersClassData
NC/benchmark/methods/RSHN/torch_geometric/data/data.py:8
↓ 7 callersClassData
NC/RSHN/torch_geometric/data/data.py:8
↓ 7 callersClassindex_generator
LP/benchmark/methods/MAGNN_ini/utils/tools.py:212
↓ 6 callersClassEarlyStopping
Early stops the training if validation loss doesn't improve after a given patience.
NC/benchmark/methods/MAGNN/utils/pytorchtools.py:5
↓ 6 callersClassindex_generator
NC/MAGNN/utils/tools.py:212
↓ 6 callersClassindex_generator
LP/MAGNN/utils/tools.py:213
↓ 6 callersClassindex_generator
LP/benchmark/methods/MAGNN/utils/tools.py:212
↓ 6 callersClassmyGATConv
Adapted from https://docs.dgl.ai/_modules/dgl/nn/pytorch/conv/gatconv.html#GATConv
NC/benchmark/methods/baseline/conv.py:13
↓ 4 callersClassEarlyStopping
Early stops the training if validation loss doesn't improve after a given patience.
NC/MAGNN/utils/pytorchtools.py:5
↓ 4 callersClassHANLayer
HAN layer. Arguments --------- meta_paths : list of metapaths, each as a list of edge types in_size : input feature dimension
NC/benchmark/methods/HAN/model_hetero_multi.py:35
↓ 4 callersClassHANLayer
HAN layer. Arguments --------- meta_paths : list of metapaths, each as a list of edge types in_size : input feature dimension
NC/benchmark/methods/HAN/model_hetero.py:35
↓ 4 callersClassMAGNN_ctr_ntype_specific
NC/MAGNN/model/base_MAGNN.py:185
↓ 4 callersClassMAGNN_ctr_ntype_specific
NC/benchmark/methods/MAGNN/model/base_MAGNN.py:185
↓ 4 callersClassMAGNN_ctr_ntype_specific
LP/MAGNN/model/base_MAGNN.py:185
↓ 4 callersClassMAGNN_ctr_ntype_specific
LP/benchmark/methods/MAGNN/model/base_MAGNN.py:185
↓ 4 callersClassMAGNN_ctr_ntype_specific
LP/benchmark/methods/MAGNN_ini/model/base_MAGNN.py:185
↓ 4 callersClassNNConv
NC/RSHN/torch_geometric/nn/conv/nn_conv.py:8
↓ 4 callersClassRelationConv
NC/RSHN/torch_geometric/nn/conv/relation_conv.py:8
↓ 4 callersClassmyGAT
TC/HGAT/model/code/baseline/GNN.py:10
↓ 3 callersClassDisMult
LP/RGCN/GNN.py:5
↓ 3 callersClassDot
LP/RGCN/GNN.py:24
↓ 3 callersClassEarlyStopping
Early stops the training if validation loss doesn't improve after a given patience.
NC/benchmark/methods/baseline/utils/pytorchtools.py:5
↓ 3 callersClassEarlyStopping
Early stops the training if validation loss doesn't improve after a given patience.
LP/benchmark/methods/MAGNN/utils/pytorchtools.py:5
↓ 3 callersClassEarlyStopping
Early stops the training if validation loss doesn't improve after a given patience.
LP/benchmark/methods/MAGNN_ini/utils/pytorchtools.py:5
↓ 3 callersClassEarlyStopping
Early stops the training if validation loss doesn't improve after a given patience.
TC/HGAT/model/code/baseline/utils/pytorchtools.py:5
↓ 3 callersClassGTConv
NC/benchmark/methods/GTN/model_sparse.py:118
↓ 3 callersClassGTConv
NC/benchmark/methods/GTN/model.py:124
↓ 3 callersClassGTConv
NC/GTN/model_sparse.py:118
↓ 3 callersClassGTConv
NC/GTN/model.py:119
↓ 3 callersClassGraphConvolution
Graph convolution layer.
Recom/KGNN-LS/src/gcn/layers.py:14
↓ 3 callersClassLogger
TC/HGAT/model/code/print_log.py:5
↓ 3 callersClassMAGNN_nc_mb
NC/benchmark/methods/MAGNN/model/MAGNN_nc_mb.py:63
↓ 3 callersClassMyGraphConvolution
TC/HGAT/model/code/layers.py:9
↓ 3 callersClassmyGATConv
Adapted from https://docs.dgl.ai/_modules/dgl/nn/pytorch/conv/gatconv.html#GATConv
LP/benchmark/methods/baseline/conv.py:13
↓ 3 callersClassmyGATConv
Adapted from https://docs.dgl.ai/_modules/dgl/nn/pytorch/conv/gatconv.html#GATConv
TC/HGAT/model/code/baseline/conv.py:13
↓ 3 callersClassmyGATConv
Adapted from https://docs.dgl.ai/_modules/dgl/nn/pytorch/conv/gatconv.html#GATConv
Recom/baseline/Model/conv.py:13
↓ 2 callersClassBatch
NC/benchmark/methods/RSHN/torch_geometric/data/batch.py:5
↓ 2 callersClassBatch
NC/RSHN/torch_geometric/data/batch.py:5
↓ 2 callersClassDataLoader
NC/RSHN/torch_geometric/data/dataloader.py:7
↓ 2 callersClassDisMult
LP/RGCN/model.py:11
↓ 2 callersClassDisMult
LP/HetGNN/code/homoGNN.py:21
↓ 2 callersClassDisMult
LP/RGCN-WN18/code/model.py:102
↓ 2 callersClassDisMult
LP/benchmark/methods/GNN/GNN.py:5
↓ 2 callersClassDisMult
LP/GATNE/src/homGNN.py:109
↓ 2 callersClassDot
LP/HetGNN/code/homoGNN.py:41
↓ 2 callersClassDot
LP/RGCN-WN18/code/model.py:121
↓ 2 callersClassDot
LP/benchmark/methods/GNN/GNN.py:24
↓ 2 callersClassEarlyStopping
Early stops the training if validation loss doesn't improve after a given patience.
NC/benchmark/methods/HGT/utils/pytorchtools.py:5
↓ 2 callersClassEarlyStopping
NC/benchmark/methods/HAN/utils.py:351
↓ 2 callersClassEarlyStopping
Early stops the training if validation loss doesn't improve after a given patience.
NC/benchmark/methods/GNN/utils/pytorchtools.py:5
↓ 2 callersClassEarlyStopping
Early stops the training if validation loss doesn't improve after a given patience.
LP/MAGNN/utils/pytorchtools.py:5
↓ 2 callersClassEarlyStopping
Early stops the training if validation loss doesn't improve after a given patience.
LP/RGCN-WN18/code/utils.py:9
↓ 2 callersClassEarlyStopping
Early stops the training if validation loss doesn't improve after a given patience.
LP/benchmark/methods/baseline/utils/pytorchtools.py:5
↓ 2 callersClassEntities
r"""The relational entities networks "AIFB", "MUTAG", "BGS" and "AM" from the `"Modeling Relational Data with Graph Convolutional Networks" <h
NC/RSHN/torch_geometric/datasets/entities.py:16
↓ 2 callersClassGAT
NC/benchmark/methods/GNN/GNN.py:10
↓ 2 callersClassGAT
LP/HetGNN/code/homoGNN.py:77
↓ 2 callersClassGCN
NC/benchmark/methods/GNN/GNN.py:58
↓ 2 callersClassGCN
LP/HetGNN/code/homoGNN.py:50
↓ 2 callersClassGTLayer
NC/benchmark/methods/GTN/model_sparse.py:86
↓ 2 callersClassGTLayer
NC/benchmark/methods/GTN/model.py:99
↓ 2 callersClassGTLayer
NC/GTN/model_sparse.py:86
↓ 2 callersClassGTLayer
NC/GTN/model.py:94
↓ 2 callersClassGTN
NC/benchmark/methods/GTN/model.py:10
↓ 2 callersClassGraphConvolution
TC/HGAT/model/code/layers.py:47
↓ 2 callersClassGraphConvolution
Graph convolution layer.
Recom/KGCN/src/gcn/layers.py:14
↓ 2 callersClassHANLayer
HAN layer. Arguments --------- meta_paths : list of metapaths, each as a list of edge types in_size : input feature dimension
NC/HAN/model_hetero.py:34
↓ 2 callersClassHANLayer
HAN layer. Arguments --------- num_meta_paths : number of homogeneous graphs generated from the metapaths. in_size : input featu
NC/HAN/model.py:24
↓ 2 callersClassMAGNN_lp
LP/benchmark/methods/MAGNN_ini/model/MAGNN_lp.py:79
↓ 2 callersClassMAGNN_nc
NC/benchmark/methods/MAGNN/model/MAGNN_nc.py:80
↓ 2 callersClassMAGNN_nc_layer
NC/MAGNN/model/MAGNN_nc.py:12
↓ 2 callersClassMAGNN_nc_layer
NC/benchmark/methods/MAGNN/model/MAGNN_nc.py:12
↓ 2 callersClassMAGNN_nc_layer
LP/MAGNN/model/MAGNN_nc.py:12
↓ 2 callersClassMAGNN_nc_layer
LP/benchmark/methods/MAGNN/model/MAGNN_nc.py:12
↓ 2 callersClassMAGNN_nc_layer
LP/benchmark/methods/MAGNN_ini/model/MAGNN_nc.py:12
↓ 2 callersClassNNConv
NC/benchmark/methods/RSHN/torch_geometric/nn/conv/nn_conv.py:8
↓ 2 callersClassRelationConv
NC/benchmark/methods/RSHN/torch_geometric/nn/conv/relation_conv.py:8
↓ 2 callersClassSelfAttention
TC/HGAT/model/code/layers.py:105
↓ 2 callersClassSumAggregator
Recom/KGNN-LS/src/aggregators.py:68
↓ 2 callersClassdata_loader
NC/benchmark/scripts/data_loader.py:22
↓ 2 callersClassdata_loader
NC/benchmark/methods/RGCN/scripts/data_loader.py:15
↓ 2 callersClassedge_data
LP/HetGNN/code/homoGNN.py:110
↓ 2 callersClassmyGAT
NC/benchmark/methods/baseline/GNN.py:10
↓ 2 callersClassmyGAT
LP/benchmark/methods/baseline/GNN.py:30
↓ 2 callersClassweighted_GCN
TC/HGAT/model/code/models.py:12
↓ 1 callersClassAUC_MRR
LP/benchmark/scripts/LP_AUC_MRR.py:9
↓ 1 callersClassAttention_NodeLevel
TC/HGAT/model/code/layers.py:180
↓ 1 callersClassBPRMF
Recom/KGAT/Model/BPRMF.py:11
↓ 1 callersClassBPRMF_loader
Recom/KGAT/Model/utility/loader_bprmf.py:9
↓ 1 callersClassCFKG
Recom/KGAT/Model/CFKG.py:11
↓ 1 callersClassCFKG_loader
Recom/KGAT/Model/utility/loader_cfkg.py:14
↓ 1 callersClassCKE
Recom/KGAT/Model/CKE.py:11
↓ 1 callersClassCKE_loader
Recom/KGAT/Model/utility/loader_cke.py:11
↓ 1 callersClassDistMult
LP/benchmark/methods/HGT/model.py:8
↓ 1 callersClassDistMult
LP/benchmark/methods/baseline/GNN.py:10
↓ 1 callersClassDot
LP/benchmark/methods/HGT/model.py:20
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