Merge branch 'main' into main

openhgnn/config.ini

@@ -1,3 +1,96 @@
+[DisenKGAT]
+#   str
+name = Disen_Model
+#   data =  DisenKGAT_WN18RR
+#   model = DisenKGAT
+score_func = interacte
+opn = cross
+#  gpu = 2
+logdir = ./log/
+config = ./config/
+strategy = one_to_n
+form = plain
+mi_method = club_b
+att_mode = dot_weight
+score_method = dot_rel
+score_order = after
+gamma_method = norm
+
+
+#   int
+k_w = 10
+batch = 2048
+test_batch = 2048
+epoch = 1500
+num_workers = 10
+seed = 41504
+init_dim = 100
+gcn_dim = 200
+embed_dim = 200
+gcn_layer = 1
+k_h = 20
+num_filt = 200
+ker_sz = 7
+num_bases = -1
+neg_num = 1000
+ik_w = 10
+ik_h = 20
+inum_filt = 200
+iker_sz = 9
+iperm = 1
+head_num = 1
+num_factors = 3
+early_stop = 200
+mi_epoch = 1
+
+#   float
+feat_drop = 0.3
+hid_drop2 = 0.3
+hid_drop = 0.3
+gcn_drop = 0.4
+gamma = 9.0
+l2 = 0.0
+lr = 0.001
+lbl_smooth = 0.1
+iinp_drop = 0.3
+ifeat_drop = 0.4
+ihid_drop = 0.3
+alpha = 1e-1
+max_gamma = 5.0
+init_gamma = 9.0
+
+#   boolean
+restore = False
+bias = False
+no_act = False
+mi_train = True
+no_enc = False
+mi_drop = True
+fix_gamma = False
+
+
+
+
+
+[NBF]
+input_dim = 32
+hidden_dims = [32, 32, 32, 32, 32, 32]
+message_func = distmult
+aggregate_func = pna
+short_cut = True
+layer_norm = True
+dependent = False
+num_negative = 32
+strict_negative = True
+adversarial_temperature = 1
+metric = ['mr', 'mrr', 'hits@1', 'hits@3', 'hits@10', 'hits@10_50']
+lr = 0.005
+gpus = [0]
+batch_size = 64
+num_epoch = 20
+log_interval = 100
+
+
 [General]
 learning_rate = 0.01
 weight_decay = 0.0001
@@ -887,3 +980,254 @@ embedding_size = 64
 num_layers = 3
 test_u_batch_size = 100
 topks = 20
+
+[Grail]
+num_epochs: 100
+eval_every: 3
+eval_every_iter: 455
+save_every: 10
+early_stop: 100
+optimizer: Adam
+lr: 0.01
+clip:1000
+l2: 5e-4
+margin: 10
+max_links:1000000
+hop: 3
+max_nodes_per_hop: 0
+use_kge_embeddings: False
+kge_model: TransE
+model_type: dgl
+constrained_neg_prob: 0.0
+batch_size: 16
+num_neg_samples_per_link: 1
+num_workers: 8
+add_traspose_rels: False
+enclosing_sub_graph: True
+rel_emb_dim: 32
+attn_rel_emb_dim: 32
+emb_dim: 32
+num_gcn_layers: 3
+num_bases: 4
+dropout: 0
+edge_dropout: 0.5
+gnn_agg_type: sum
+add_ht_emb: True
+has_attn: True
+mode: sample
+
+[ComPILE]
+num_epochs: 100
+eval_every: 3
+eval_every_iter: 455
+save_every: 10
+early_stop: 100
+optimizer: Adam
+lr: 0.01
+clip:1000
+l2: 5e-4
+margin: 10
+max_links:1000000
+hop: 3
+max_nodes_per_hop: 0
+use_kge_embeddings: False
+kge_model: TransE
+model_type: dgl
+constrained_neg_prob: 0.0
+batch_size: 16
+num_neg_samples_per_link: 1
+num_workers: 8
+add_traspose_rels: False
+enclosing_sub_graph: True
+rel_emb_dim: 32
+attn_rel_emb_dim: 32
+emb_dim: 32
+num_gcn_layers: 3
+num_bases: 4
+dropout: 0
+edge_dropout: 0.5
+gnn_agg_type: sum
+add_ht_emb: True
+has_attn: True
+mode: sample
+
+
+[AdapropT]
+data_path = data/family/
+layers=8
+sampling=incremental
+act=relu
+weight=None
+tau=1.0
+train=True
+remove_1hop_edges=False
+scheduler=exp
+fact_ratio=0.9
+epoch=300
+eval_interval=1
+topk = 100
+lr = 0.0036
+decay_rate = 0.999
+lamb = 0.000017
+hidden_dim = 48
+attn_dim = 5
+dropout = 0.29
+n_edge_topk = -1
+n_layer = 8
+n_batch = 20
+n_node_topk = 800
+seed = 1234
+n_tbatch=20
+eval=False
+[AdapropI]
+data_path=./data/fb237_v1
+seed=1234
+
+[LTE]
+model_name_GCN=LTE_Transe
+model_name=LTE
+name=lte
+data=FB15k-237
+score_func=transe
+opn=mult
+hid_drop=0.2
+gpu=0
+x_ops=p
+n_layer=0
+init_dim=200
+batch_size=64
+epoch=300
+l2=0.0
+lr=0.001
+lbl_smooth=0.1
+num_workers=8
+seed=12345
+restore=False
+bias=False
+num_bases=-1
+gcn_dim=200
+gcn_drop=0.1
+conve_hid_drop=0.3
+feat_drop=0.2
+input_drop=0.2
+k_w=20
+k_h=10
+num_filt=200
+ker_sz=7
+gamma=9.0
+rat=False
+wni=False
+wsi=False
+ss=False
+nobn=False
+noltr=False
+encoder=compgcn
+max_epochs=500
+
+[SACN]
+seed=12345
+init_emb_size=200
+gc1_emb_size=150
+embedding_dim=200
+input_dropout=0
+dropout_rate=0.2
+channels=200
+kernel_size=5
+gpu=5
+lr=0.002
+n_epochs=300
+num_workers=2
+eval_every=1
+dataset_data=FB15k-237
+batch_size=64
+patience=100
+decoder=transe
+gamma=9.0
+name=repro
+n_layer=1
+rat=False
+wsi=False
+wni=False
+ss=-1
+final_act=True
+final_bn=False
+final_drop=False
+
+[ExpressGNN]
+seed=10
+embedding_size = 128
+gcn_free_size = 127
+slice_dim = 16
+no_train = 0
+filtered = filtered
+hidden_dim = 64
+num_epochs = 100
+batchsize = 16
+trans = 0
+num_hops = 2
+num_mlp_layers = 2
+num_batches = 100
+learning_rate = 0.0005
+lr_decay_factor = 0.5
+lr_decay_patience = 100
+lr_decay_min = 0.00001
+patience = 20
+l2_coef = 0.0
+observed_prob = 0.9
+entropy_temp = 1
+no_entropy = 0
+rule_weights_learning = 1
+learning_rate_rule_weights = 0.001
+epoch_mode = 0
+shuffle_sampling = 1
+load_method = 1
+load_s = 1
+use_gcn = 1
+filter_latent = 0
+closed_world = 0
+
+[Ingram]
+margin = 2
+lr = 5e-4
+nle = 2
+nlr = 2
+d_e = 32
+d_r = 32
+hdr_e = 8
+hdr_r = 4
+num_bin = 10
+num_epoch = 10000
+validation_epoch = 200
+num_head = 8
+num_neg = 10
+
+[RedGNN]
+seed = 0
+patience = 3
+batch_size = 100
+hidden_dim = 64
+optimizer = Adam
+lr = 0.005
+weight_decay = 0.0002
+max_epoch = 50
+decay_rate = 0.991
+attn_dim = 5
+dropout = 0.21
+act = idd
+n_layer = 5
+
+[RedGNNT]
+seed = 0
+patience = 3
+batch_size = 20
+hidden_dim = 48
+optimizer = Adam
+lr = 0.0036
+weight_decay = 0.000017
+max_epoch = 50
+decay_rate = 0.999
+attn_dim = 5
+dropout = 0.21
+act = relu
+n_layer = 3
+n_tbatch = 50