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Functions2,023 in github.com/THUDM/HGB

⨍ Functions 2,023◇ Types & classes 293
↓ 2 callersFunctionpreprocess_adj_bias
(adj, norm=False)
Recom/KGNN-LS/src/model.py:18
↓ 2 callersFunctionpreprocess_corpus_notDropEntity
(corpus, stopwords, involved_entity)
TC/HGAT/utils.py:70
↓ 2 callersFunctionread_args
()
NC/HetGNN/code/args.py:3
↓ 2 callersFunctionread_args
()
NC/benchmark/methods/HetGNN/code/DBLP/args.py:3
↓ 2 callersFunctionread_args
()
NC/benchmark/methods/HetGNN/code/ACM/args.py:4
↓ 2 callersFunctionread_args
()
NC/benchmark/methods/HetGNN/code/IMDB/args.py:4
↓ 2 callersFunctionread_args
()
LP/HetGNN/code/args.py:3
↓ 2 callersFunctionread_args
()
LP/benchmark/methods/HetGNN/args.py:4
↓ 2 callersFunctionread_dictionary
(filename, id_lookup=True)
LP/RGCN-WN18/code/scripts/read_file.py:5
↓ 2 callersFunctionread_triplets
(filename)
LP/RGCN-WN18/code/scripts/read_file.py:19
↓ 2 callersFunctionreorder
(neigh, label)
LP/benchmark/methods/MAGNN_ini/test_LastFM.py:113
↓ 2 callersFunctionrepeat
(src, length)
NC/benchmark/methods/RSHN/torch_geometric/utils/repeat.py:5
↓ 2 callersFunctionrepeat
(src, length)
NC/RSHN/torch_geometric/utils/repeat.py:5
↓ 2 callersMethodreset_parameters
(self)
NC/benchmark/methods/RSHN/torch_geometric/nn/conv/nn_conv.py:36
↓ 2 callersMethodreset_parameters
(self)
NC/benchmark/methods/GTN/model.py:35
↓ 2 callersMethodreset_parameters
(self)
NC/RSHN/torch_geometric/nn/conv/nn_conv.py:36
↓ 2 callersMethodreset_parameters
(self)
NC/GTN/model.py:35
↓ 2 callersMethodreset_parameters
(self)
LP/RGCN/model.py:18
↓ 2 callersMethodsave_checkpoint
Saves model when validation loss decrease.
NC/MAGNN/utils/pytorchtools.py:43
↓ 2 callersMethodsave_checkpoint
Saves model when validation loss decrease.
NC/RGCN/model.py:183
↓ 2 callersMethodsave_checkpoint
Saves model when validation loss decreases.
NC/HetGNN/code/homoGNN.py:46
↓ 2 callersMethodsave_checkpoint
Saves model when validation loss decrease.
NC/benchmark/methods/MAGNN/utils/pytorchtools.py:43
↓ 2 callersMethodsave_checkpoint
Saves model when validation loss decrease.
NC/benchmark/methods/HGT/utils/pytorchtools.py:43
↓ 2 callersMethodsave_checkpoint
Saves model when validation loss decrease.
NC/benchmark/methods/baseline/utils/pytorchtools.py:43
↓ 2 callersMethodsave_checkpoint
Saves model when validation loss decreases.
NC/benchmark/methods/HAN/utils.py:380
↓ 2 callersMethodsave_checkpoint
Saves model when validation loss decrease.
NC/benchmark/methods/GNN/utils/pytorchtools.py:43
↓ 2 callersMethodsave_checkpoint
Saves model when validation loss decreases.
NC/HAN/utils.py:260
↓ 2 callersMethodsave_checkpoint
Saves model when validation loss decrease.
LP/MAGNN/utils/pytorchtools.py:43
↓ 2 callersMethodsave_checkpoint
Saves model when validation loss decrease.
LP/RGCN-WN18/code/utils.py:49
↓ 2 callersMethodsave_checkpoint
Saves model when validation loss decrease.
LP/benchmark/methods/MAGNN/utils/pytorchtools.py:43
↓ 2 callersMethodsave_checkpoint
Saves model when validation loss decrease.
LP/benchmark/methods/HGT/utils/pytorchtools.py:43
↓ 2 callersMethodsave_checkpoint
Saves model when validation loss decrease.
LP/benchmark/methods/RGCN/utils.py:46
↓ 2 callersMethodsave_checkpoint
Saves model when validation loss decrease.
LP/benchmark/methods/MAGNN_ini/utils/pytorchtools.py:43
↓ 2 callersMethodsave_checkpoint
Saves model when validation loss decrease.
LP/benchmark/methods/baseline/utils/pytorchtools.py:43
↓ 2 callersMethodsave_checkpoint
Saves model when validation loss decrease.
TC/HGAT/model/code/baseline/utils/pytorchtools.py:43
↓ 2 callersFunctionscatter_
r"""Aggregates all values from the :attr:`src` tensor at the indices specified in the :attr:`index` tensor along the first dimension. If multi
NC/benchmark/methods/RSHN/torch_geometric/utils/scatter.py:4
↓ 2 callersFunctionscatter_
r"""Aggregates all values from the :attr:`src` tensor at the indices specified in the :attr:`index` tensor along the first dimension. If multi
NC/RSHN/torch_geometric/utils/scatter.py:4
↓ 2 callersFunctionscore
(logits, labels)
NC/MAGNN/run_DBLP_gnn.py:16
↓ 2 callersFunctionscore
(logits, labels)
NC/HetGNN/code/homoGNN.py:301
↓ 2 callersFunctionscore
(logits, labels)
NC/benchmark/methods/HAN/main.py:9
↓ 2 callersFunctionscore
(logits, labels)
NC/HAN/main.py:9
↓ 2 callersFunctionset_random_seed
Set random seed. Parameters ---------- seed : int Random seed to use
NC/benchmark/methods/HAN/utils.py:23
↓ 2 callersFunctionsetup
(args)
NC/benchmark/methods/HAN/utils.py:109
↓ 2 callersFunctionsetup_log_dir
Name and create directory for logging. Parameters ---------- args : dict Configuration Returns ------- log_dir : str
NC/benchmark/methods/HAN/utils.py:70
↓ 2 callersFunctionsetup_log_dir
Name and create directory for logging. Parameters ---------- args : dict Configuration Returns ------- log_dir : str
NC/HAN/utils.py:59
↓ 2 callersFunctionshuffle
(neigh, label)
LP/benchmark/methods/MAGNN_ini/test_LastFM.py:84
↓ 2 callersFunctionsort_and_rank
(out_feat, args)
LP/RGCN/HomGNN.py:77
↓ 2 callersFunctionsp_to_spt
(mat)
NC/benchmark/methods/RSHN/RSHN.py:131
↓ 2 callersFunctionsparse_mx_to_torch_sparse_tensor
Convert a scipy sparse matrix to a torch sparse tensor.
TC/HGAT/model/code/utils.py:230
↓ 2 callersFunctionsparse_to_tuple
Convert sparse matrix to tuple representation.
NC/benchmark/methods/HetSANN/HetSANN_MRV/utils/process.py:55
↓ 2 callersFunctionsymmetric
(directed_adjacency, clip_to_one=True)
NC/benchmark/methods/RSHN/build_coarsened_line_graph/utils.py:165
↓ 2 callersFunctionsymmetric
(directed_adjacency, clip_to_one=True)
NC/RSHN/build_coarsened_line_graph/utils.py:163
↓ 2 callersMethodt_content_agg
(self, id_batch)
NC/benchmark/methods/HetGNN/code/DBLP/tools.py:93
↓ 2 callersFunctionto_list
(x)
NC/benchmark/methods/RSHN/torch_geometric/data/dataset.py:9
↓ 2 callersFunctionto_list
(x)
NC/RSHN/torch_geometric/data/dataset.py:9
↓ 2 callersFunctionto_tuple
(mx)
NC/benchmark/methods/HetSANN/HetSANN_MRV/utils/process.py:57
↓ 2 callersMethodtrans_x4_x2
(self, datax4: list)
LP/benchmark/methods/GNN/utils.py:109
↓ 2 callersFunctiontrue_positive
(pred, target, num_classes)
NC/benchmark/methods/RSHN/torch_geometric/utils/metric.py:10
↓ 2 callersFunctiontrue_positive
r"""Computes the number of true positive predictions. Args: pred (Tensor): The predictions. target (Tensor): The targets.
NC/benchmark/methods/GTN/utils.py:19
↓ 2 callersFunctiontrue_positive
(pred, target, num_classes)
NC/RSHN/torch_geometric/utils/metric.py:10
↓ 2 callersFunctiontrue_positive
r"""Computes the number of true positive predictions. Args: pred (Tensor): The predictions. target (Tensor): The targets.
NC/GTN/utils.py:19
↓ 2 callersMethodv_content_agg
(self, id_batch)
NC/HetGNN/code/tools.py:97
↓ 2 callersMethodv_content_agg
(self, id_batch)
NC/benchmark/methods/HetGNN/code/DBLP/tools.py:108
↓ 2 callersMethodv_content_agg
(self, id_batch)
LP/HetGNN/code/tools.py:97
↓ 1 callersFunctionGCN
(inputs, dim, drop, A)
Recom/KGCN/src/gcn/layers.py:57
↓ 1 callersFunctionGCN
(inputs, dim, drop, A, n_layer)
Recom/KGNN-LS/src/gcn/layers.py:58
↓ 1 callersMethod__init__
(self, g, num_layers, in_dim, num_hidden,
NC/MAGNN/GNN.py:11
↓ 1 callersMethod__init__
(self, num_layers, num_metapaths_list, num_edge_type,
NC/MAGNN/model/MAGNN_nc.py:81
↓ 1 callersMethod__init__
(self, num_metapaths_list, num_edge_type, etypes_lists,
NC/MAGNN/model/MAGNN_lp.py:80
↓ 1 callersMethod__init__
(self, etypes, out_dim, num_heads, rnn_typ
NC/MAGNN/model/base_MAGNN.py:9
↓ 1 callersMethod__init__
(self, num_metapaths, num_edge_type, etypes_list,
NC/MAGNN/model/MAGNN_nc_mb.py:64
↓ 1 callersMethod__init__
(self, in_feats, hid_feats, out_feats, n_layers=2, dropout=0.5)
NC/HetGNN/code/homoGNN.py:56
↓ 1 callersMethod__init__
(self, num_layers, num_metapaths_list, num_edge_type,
NC/benchmark/methods/MAGNN/model/MAGNN_nc.py:81
↓ 1 callersMethod__init__
(self, num_metapaths_list, num_edge_type, etypes_lists,
NC/benchmark/methods/MAGNN/model/MAGNN_lp.py:80
↓ 1 callersMethod__init__
(self, etypes, out_dim, num_heads, rnn_typ
NC/benchmark/methods/MAGNN/model/base_MAGNN.py:9
↓ 1 callersMethod__init__
(self, num_metapaths, num_edge_type, etypes_list,
NC/benchmark/methods/MAGNN/model/MAGNN_nc_mb.py:64
↓ 1 callersMethod__init__
(self, G, n_inps, n_hid, n_out, n_layers, n_heads, use_norm = True)
NC/benchmark/methods/HGT/model.py:90
↓ 1 callersMethod__init__
(self, in_dims, h_dim, out_dim, num_rels, num_bases, num_hidden_layers=1, dropout=0,
NC/benchmark/methods/RGCN/model.py:8
↓ 1 callersMethod__init__
(self, g, in_dims, num_hidden, num_classes
NC/benchmark/methods/GNN/GNN.py:11
↓ 1 callersMethod__init__
(self, dataset, batch_size=1, shuffle=True, **kwargs)
NC/benchmark/methods/RSHN/torch_geometric/data/dataloader.py:8
↓ 1 callersMethod__init__
(self, in_dim, num_hidden, num_classes, nu
NC/HAN/GNN.py:11
↓ 1 callersMethod__init__
(self, g, num_layers, in_dim, num_hidden,
NC/RSHN/model/GNN.py:11
↓ 1 callersMethod__init__
(self, dataset, batch_size=1, shuffle=True, **kwargs)
NC/RSHN/torch_geometric/data/dataloader.py:8
↓ 1 callersMethod__init__
(self, g, num_layers, in_dim, num_hidden,
NC/GTN/GNN.py:11
↓ 1 callersMethod__init__
(self, num_layers, num_metapaths_list, num_edge_type,
LP/MAGNN/model/MAGNN_nc.py:81
↓ 1 callersMethod__init__
(self, num_metapaths_list, num_edge_type, etypes_lists,
LP/MAGNN/model/MAGNN_lp.py:80
↓ 1 callersMethod__init__
(self, etypes, out_dim, num_heads, rnn_typ
LP/MAGNN/model/base_MAGNN.py:9
↓ 1 callersMethod__init__
(self, num_metapaths, num_edge_type, etypes_list,
LP/MAGNN/model/MAGNN_nc_mb.py:64
↓ 1 callersMethod__init__
(self, in_dim, h_dim, num_rels, num_bases=-1, num_hidden_layers=1, dropout=0, use_cuda=False,
LP/RGCN/link_predict.py:49
↓ 1 callersMethod__init__
(self, num_layers, num_metapaths_list, num_edge_type,
LP/benchmark/methods/MAGNN/model/MAGNN_nc.py:81
↓ 1 callersMethod__init__
(self, num_metapaths_list, num_edge_type, etypes_lists,
LP/benchmark/methods/MAGNN/model/MAGNN_lp.py:80
↓ 1 callersMethod__init__
(self, etypes, out_dim, num_heads, rnn_typ
LP/benchmark/methods/MAGNN/model/base_MAGNN.py:9
↓ 1 callersMethod__init__
(self, num_metapaths, num_edge_type, etypes_list,
LP/benchmark/methods/MAGNN/model/MAGNN_nc_mb.py:64
↓ 1 callersMethod__init__
(self, in_dims, h_dim, out_dim, num_rels, num_bases, num_hidden_layers=1, dropout=0,
LP/benchmark/methods/RGCN/model.py:8
↓ 1 callersMethod__init__
(self, num_layers, num_metapaths_list, num_edge_type,
LP/benchmark/methods/MAGNN_ini/model/MAGNN_nc.py:81
↓ 1 callersMethod__init__
(self, num_metapaths_list, num_edge_type, etypes_lists,
LP/benchmark/methods/MAGNN_ini/model/MAGNN_lp.py:80
↓ 1 callersMethod__init__
(self, etypes, out_dim, num_heads, rnn_typ
LP/benchmark/methods/MAGNN_ini/model/base_MAGNN.py:9
↓ 1 callersMethod__init__
(self, num_metapaths, num_edge_type, etypes_list,
LP/benchmark/methods/MAGNN_ini/model/MAGNN_nc_mb.py:64
↓ 1 callersMethod__init__
(self, num_nodes, num_sampled, embedding_size)
LP/benchmark/methods/GATNE/main_pytorch.py:113
↓ 1 callersMethod__init__
(self, num_nodes, num_sampled, embedding_size)
LP/GATNE/src/main_pytorch.py:108
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