<jupyter_start><jupyter_text>Pubmed Dataset<jupyter_code>Sage_183 = np.load('./pubmed/GraphSage_accuracy_list_pubmed_batchsize183.npy')
Sage_random_01 = np.load('./pubmed/GraphSage_accuracy_list_pubmed_random_ratio0.01.npy')
Sage_degree_01 = np.load('./pubmed/GraphSage_accuracy_list_pubmed_degree_ratio0.01.npy')
Sage_laplacian_01 = np.load('./pubmed/GraphSage_accuracy_list_pubmed_laplacian_ratio0.01.npy')
Sage_kmedoid_01 = np.load('./pubmed/GraphSage_accuracy_list_pubmed_Kmedoid_ncluster182_AggregateLayer3_ratio0.01.npy')
Sage_random_loss_01 = np.load('./pubmed/GraphSage_accuracy_list_pubmed_EL2N_sample_pretrain100_epochs1000_pretrain_ratio0.01_ratio0.01_sample_freq20.npy')
Sage_gradient_01 = np.load('./pubmed/GraphSage_macro_f1_list_pubmed_large_gradient_ratio0.01.npy')
Sage_Grain_ball_01 = np.load('./pubmed/GraphSage_accuracy_list_pubmed_GRAIN_ball_ratio0.01.npy')
Sage_Grain_NND_01 = np.load('./pubmed/GraphSage_accuracy_list_pubmed_GRAIN_NND_ratio0.01.npy')

pubmed_acc_183 = np.max(Sage_183)
pubmed_random_acc_01 = np.max(Sage_random_01)
pubmed_degree_acc_01 = np.max(Sage_degree_01)
pubmed_laplacian_acc_01 = np.max(Sage_laplacian_01) 
pubmed_kmedoid_acc_01 = np.max(Sage_kmedoid_01)
pubmed_random_loss_acc_01 = np.max(Sage_random_loss_01)
pubmed_gradient_acc_01 = np.max(Sage_gradient_01)
pubmed_batch_at_scale_acc_01 = pubmed_kmedoid_acc_01
pubmed_batch_at_scale_random_acc_01 = pubmed_kmedoid_acc_01
pubmed_batch_at_scale_round_robin_acc_01 = pubmed_kmedoid_acc_01
pubmed_batch_at_scale_cluster_input_acc_01 = pubmed_kmedoid_acc_01
pubmed_batch_at_scale_cluster_embed_acc_01 = pubmed_kmedoid_acc_01
pubmed_Grain_ball_acc_01 = np.max(Sage_Grain_ball_01)
pubmed_Grain_NND_acc_01 = np.max(Sage_Grain_NND_01)


Sage_random_03 = np.load('./pubmed/GraphSage_accuracy_list_pubmed_random_ratio0.03.npy')
Sage_degree_03 = np.load('./pubmed/GraphSage_accuracy_list_pubmed_degree_ratio0.03.npy')
Sage_laplacian_03 = np.load('./pubmed/GraphSage_accuracy_list_pubmed_laplacian_ratio0.03.npy')
Sage_kmedoid_03 = np.load('./pubmed/GraphSage_accuracy_list_pubmed_Kmedoid_ncluster200_PropToSdFalse_AggregateLayer3_ratio0.03.npy')
Sage_batch_at_scale_03 = np.load('./pubmed/GraphSage_accuracy_list_pubmed_batch_at_scale_sample_method_kmeans_cold_startFalse_redundancy10_pretrain_ratio0.01_ratio0.005_total_ratio0.03.npy')
Sage_batch_at_scale_random_03 = np.load('./pubmed/GraphSage_accuracy_list_pubmed_batch_at_scale_sample_method_random_choice_cold_startFalse_redundancy10_pretrain_ratio0.01_ratio0.005_total_ratio0.03.npy')
Sage_batch_at_scale_round_robin_03 = np.load('./pubmed/GraphSage_accuracy_list_pubmed_batch_at_scale_sample_method_round_robin_cold_startFalse_redundancy10_pretrain_ratio0.01_ratio0.005_total_ratio0.03.npy')
Sage_batch_at_scale_cluster_input_03 = np.load('./pubmed/GraphSage_accuracy_list_pubmed_batch_at_scale_sample_method_cluster_input_cold_startFalse_redundancy10_pretrain_ratio0.01_ratio0.005_total_ratio0.03.npy')
Sage_batch_at_scale_cluster_embed_03 = np.load('./pubmed/GraphSage_accuracy_list_pubmed_batch_at_scale_sample_method_cluster_embed_cold_startFalse_redundancy10_pretrain_ratio0.01_ratio0.005_total_ratio0.03.npy')
Sage_Grain_ball_03 = np.load('./pubmed/GraphSage_accuracy_list_pubmed_GRAIN_ball_ratio0.03.npy')
Sage_Grain_NND_03 = np.load('./pubmed/GraphSage_accuracy_list_pubmed_GRAIN_NND_ratio0.03.npy')
Sage_AGE_03 = np.load('./pubmed/GraphSage_accuracy_list_pubmed_AGE_ratio0.03.npy')
Sage_ANRMAB_03 = np.load('./pubmed/GraphSage_accuracy_list_pubmed_ANRMAB_ratio0.03.npy')


pubmed_random_acc_03 = np.max(Sage_random_03)
pubmed_degree_acc_03 = np.max(Sage_degree_03)
pubmed_laplacian_acc_03 = np.max(Sage_laplacian_03) 
pubmed_kmedoid_acc_03 = np.max(Sage_kmedoid_03)
pubmed_batch_at_scale_acc_03 = np.max(Sage_batch_at_scale_03)
pubmed_batch_at_scale_random_acc_03 = np.max(Sage_batch_at_scale_random_03)
pubmed_batch_at_scale_round_robin_acc_03 = np.max(Sage_batch_at_scale_round_robin_03)
pubmed_batch_at_scale_cluster_input_acc_03 = np.max(Sage_batch_at_scale_cluster_input_03)
pubmed_batch_at_scale_cluster_embed_acc_03 = np.max(Sage_batch_at_scale_cluster_embed_03)
pubmed_Grain_ball_acc_03 = np.max(Sage_Grain_ball_03)
pubmed_Grain_NND_acc_03 = np.max(Sage_Grain_NND_03)
pubmed_AGE_03 = np.max(Sage_AGE_03)
pubmed_ANRMAB_03 = np.max(Sage_ANRMAB_03)


Sage_random_04 = np.load('./pubmed/GraphSage_accuracy_list_pubmed_random_ratio0.04.npy')
Sage_degree_04 = np.load('./pubmed/GraphSage_accuracy_list_pubmed_degree_ratio0.04.npy')
Sage_laplacian_04 = np.load('./pubmed/GraphSage_accuracy_list_pubmed_laplacian_ratio0.04.npy')
Sage_kmedoid_04 = np.load('./pubmed/GraphSage_accuracy_list_pubmed_Kmedoid_ncluster200_PropToSdFalse_AggregateLayer3_ratio0.04.npy')
Sage_batch_at_scale_04 = np.load('./pubmed/GraphSage_accuracy_list_pubmed_batch_at_scale_sample_method_kmeans_cold_startFalse_redundancy10_pretrain_ratio0.01_ratio0.005_total_ratio0.04.npy')
Sage_batch_at_scale_random_04 = np.load('./pubmed/GraphSage_accuracy_list_pubmed_batch_at_scale_sample_method_random_choice_cold_startFalse_redundancy10_pretrain_ratio0.01_ratio0.005_total_ratio0.04.npy')
Sage_batch_at_scale_round_robin_04 = np.load('./pubmed/GraphSage_accuracy_list_pubmed_batch_at_scale_sample_method_round_robin_cold_startFalse_redundancy10_pretrain_ratio0.01_ratio0.005_total_ratio0.04.npy')
Sage_batch_at_scale_cluster_input_04 = np.load('./pubmed/GraphSage_accuracy_list_pubmed_batch_at_scale_sample_method_cluster_input_cold_startFalse_redundancy10_pretrain_ratio0.01_ratio0.005_total_ratio0.04.npy')
Sage_batch_at_scale_cluster_embed_04 = np.load('./pubmed/GraphSage_accuracy_list_pubmed_batch_at_scale_sample_method_cluster_embed_cold_startFalse_redundancy10_pretrain_ratio0.01_ratio0.005_total_ratio0.04.npy')
Sage_Grain_ball_04 = np.load('./pubmed/GraphSage_accuracy_list_pubmed_GRAIN_ball_ratio0.04.npy')
Sage_Grain_NND_04 = np.load('./pubmed/GraphSage_accuracy_list_pubmed_GRAIN_NND_ratio0.04.npy')
Sage_AGE_04 = np.load('./pubmed/GraphSage_accuracy_list_pubmed_AGE_ratio0.04.npy')
Sage_ANRMAB_04 = np.load('./pubmed/GraphSage_accuracy_list_pubmed_ANRMAB_ratio0.04.npy')


pubmed_random_acc_04 = np.max(Sage_random_04)
pubmed_degree_acc_04 = np.max(Sage_degree_04)
pubmed_laplacian_acc_04 = np.max(Sage_laplacian_04) 
pubmed_kmedoid_acc_04 = np.max(Sage_kmedoid_04)
pubmed_batch_at_scale_acc_04 = np.max(Sage_batch_at_scale_04)
pubmed_batch_at_scale_random_acc_04 = np.max(Sage_batch_at_scale_random_04)
pubmed_batch_at_scale_round_robin_acc_04 = np.max(Sage_batch_at_scale_round_robin_04)
pubmed_batch_at_scale_cluster_input_acc_04 = np.max(Sage_batch_at_scale_cluster_input_04)
pubmed_batch_at_scale_cluster_embed_acc_04 = np.max(Sage_batch_at_scale_cluster_embed_04)
pubmed_Grain_ball_acc_04 = np.max(Sage_Grain_ball_04)
pubmed_Grain_NND_acc_04 = np.max(Sage_Grain_NND_04)
pubmed_AGE_04 = np.max(Sage_AGE_04)
pubmed_ANRMAB_04 = np.max(Sage_ANRMAB_04)



Sage_random_05 = np.load('./pubmed/GraphSage_accuracy_list_pubmed_random_ratio0.05.npy')
Sage_degree_05 = np.load('./pubmed/GraphSage_accuracy_list_pubmed_degree_ratio0.05.npy')
Sage_laplacian_05 = np.load('./pubmed/GraphSage_accuracy_list_pubmed_laplacian_ratio0.05.npy')
Sage_kmedoid_05 = np.load('./pubmed/GraphSage_accuracy_list_pubmed_Kmedoid_ncluster200_AggregateLayer3_ratio0.05.npy')
Sage_random_loss_05 = np.load('./pubmed/GraphSage_accuracy_list_pubmed_EL2N_sample_pretrain100_epochs600_pretrain_ratio0.05_ratio0.01_sample_freq30.npy')
Sage_batch_at_scale_05 = np.load('./pubmed/GraphSage_accuracy_list_pubmed_batch_at_scale_sample_method_kmeans_cold_startFalse_redundancy20_pretrain_ratio0.01_ratio0.005_total_ratio0.05.npy')
Sage_batch_at_scale_random_05 = np.load('./pubmed/GraphSage_accuracy_list_pubmed_batch_at_scale_sample_method_random_choice_cold_startFalse_redundancy20_pretrain_ratio0.01_ratio0.005_total_ratio0.05.npy')
Sage_batch_at_scale_round_robin_05 = np.load('./pubmed/GraphSage_accuracy_list_pubmed_batch_at_scale_sample_method_round_robin_cold_startFalse_redundancy20_pretrain_ratio0.01_ratio0.005_total_ratio0.05.npy')
Sage_batch_at_scale_cluster_input_05 = np.load('./pubmed/GraphSage_accuracy_list_pubmed_batch_at_scale_sample_method_cluster_input_cold_startFalse_redundancy20_pretrain_ratio0.01_ratio0.005_total_ratio0.05.npy')
Sage_batch_at_scale_cluster_embed_05 = np.load('./pubmed/GraphSage_accuracy_list_pubmed_batch_at_scale_sample_method_cluster_embed_cold_startFalse_redundancy20_pretrain_ratio0.01_ratio0.005_total_ratio0.05.npy')
Sage_Grain_ball_05 = np.load('./pubmed/GraphSage_accuracy_list_pubmed_GRAIN_ball_ratio0.05.npy')
Sage_Grain_NND_05 = np.load('./pubmed/GraphSage_accuracy_list_pubmed_GRAIN_NND_ratio0.05.npy')
Sage_AGE_05 = np.load('./pubmed/GraphSage_accuracy_list_pubmed_AGE_ratio0.05.npy')
Sage_ANRMAB_05 = np.load('./pubmed/GraphSage_accuracy_list_pubmed_ANRMAB_ratio0.05.npy')


pubmed_random_acc_05 = np.max(Sage_random_05)
pubmed_degree_acc_05 = np.max(Sage_degree_05)
pubmed_laplacian_acc_05 = np.max(Sage_laplacian_05) 
pubmed_kmedoid_acc_05 = np.max(Sage_kmedoid_05)
pubmed_random_loss_acc_05 = np.max(Sage_random_loss_05)
pubmed_batch_at_scale_acc_05 = np.max(Sage_batch_at_scale_05)
pubmed_batch_at_scale_random_acc_05 = np.max(Sage_batch_at_scale_random_05)
pubmed_batch_at_scale_round_robin_acc_05 = np.max(Sage_batch_at_scale_round_robin_05)
pubmed_batch_at_scale_cluster_input_acc_05 = np.max(Sage_batch_at_scale_cluster_input_05)
pubmed_batch_at_scale_cluster_embed_acc_05 = np.max(Sage_batch_at_scale_cluster_embed_05)
pubmed_Grain_ball_acc_05 = np.max(Sage_Grain_ball_05)
pubmed_Grain_NND_acc_05 = np.max(Sage_Grain_NND_05)
pubmed_AGE_05 = np.max(Sage_AGE_05)
pubmed_ANRMAB_05 = np.max(Sage_ANRMAB_05)
Sage_random_07 = np.load('./pubmed/GraphSage_accuracy_list_pubmed_random_ratio0.07.npy')
Sage_degree_07 = np.load('./pubmed/GraphSage_accuracy_list_pubmed_degree_ratio0.07.npy')
Sage_laplacian_07 = np.load('./pubmed/GraphSage_accuracy_list_pubmed_laplacian_ratio0.07.npy')
Sage_kmedoid_07 = np.load('./pubmed/GraphSage_accuracy_list_pubmed_Kmedoid_ncluster200_PropToSdFalse_AggregateLayer3_ratio0.07.npy')
Sage_batch_at_scale_07 = np.load('./pubmed/GraphSage_accuracy_list_pubmed_batch_at_scale_sample_method_kmeans_cold_startFalse_redundancy10_pretrain_ratio0.01_ratio0.005_total_ratio0.07.npy')
Sage_batch_at_scale_random_07 = np.load('./pubmed/GraphSage_accuracy_list_pubmed_batch_at_scale_sample_method_random_choice_cold_startFalse_redundancy10_pretrain_ratio0.01_ratio0.005_total_ratio0.07.npy')
Sage_batch_at_scale_round_robin_07 = np.load('./pubmed/GraphSage_accuracy_list_pubmed_batch_at_scale_sample_method_round_robin_cold_startFalse_redundancy10_pretrain_ratio0.01_ratio0.005_total_ratio0.07.npy')
Sage_batch_at_scale_cluster_input_07 = np.load('./pubmed/GraphSage_accuracy_list_pubmed_batch_at_scale_sample_method_cluster_input_cold_startFalse_redundancy10_pretrain_ratio0.01_ratio0.005_total_ratio0.07.npy')
Sage_batch_at_scale_cluster_embed_07 = np.load('./pubmed/GraphSage_accuracy_list_pubmed_batch_at_scale_sample_method_cluster_embed_cold_startFalse_redundancy10_pretrain_ratio0.01_ratio0.005_total_ratio0.07.npy')
Sage_Grain_ball_07 = np.load('./pubmed/GraphSage_accuracy_list_pubmed_GRAIN_ball_ratio0.07.npy')
Sage_Grain_NND_07 = np.load('./pubmed/GraphSage_accuracy_list_pubmed_GRAIN_NND_ratio0.07.npy')
Sage_AGE_07 = np.load('./pubmed/GraphSage_accuracy_list_pubmed_AGE_ratio0.07.npy')
Sage_ANRMAB_07 = np.load('./pubmed/GraphSage_accuracy_list_pubmed_ANRMAB_ratio0.07.npy')


pubmed_random_acc_07 = np.max(Sage_random_07)
pubmed_degree_acc_07 = np.max(Sage_degree_07)
pubmed_laplacian_acc_07 = np.max(Sage_laplacian_07) 
pubmed_kmedoid_acc_07 = np.max(Sage_kmedoid_07)
pubmed_batch_at_scale_acc_07 = np.max(Sage_batch_at_scale_07)
pubmed_batch_at_scale_random_acc_07 = np.max(Sage_batch_at_scale_random_07)
pubmed_batch_at_scale_round_robin_acc_07 = np.max(Sage_batch_at_scale_round_robin_07)
pubmed_batch_at_scale_cluster_input_acc_07 = np.max(Sage_batch_at_scale_cluster_input_07)
pubmed_batch_at_scale_cluster_embed_acc_07 = np.max(Sage_batch_at_scale_cluster_embed_07)
pubmed_Grain_ball_acc_07 = np.max(Sage_Grain_ball_07)
pubmed_Grain_NND_acc_07 = np.max(Sage_Grain_NND_07)
pubmed_AGE_07 = np.max(Sage_AGE_07)
pubmed_ANRMAB_07 = np.max(Sage_ANRMAB_07)


Sage_random_08 = np.load('./pubmed/GraphSage_accuracy_list_pubmed_random_ratio0.08.npy')
Sage_degree_08 = np.load('./pubmed/GraphSage_accuracy_list_pubmed_degree_ratio0.08.npy')
Sage_laplacian_08 = np.load('./pubmed/GraphSage_accuracy_list_pubmed_laplacian_ratio0.08.npy')
Sage_kmedoid_08 = np.load('./pubmed/GraphSage_accuracy_list_pubmed_Kmedoid_ncluster200_PropToSdFalse_AggregateLayer3_ratio0.08.npy')
Sage_batch_at_scale_08 = np.load('./pubmed/GraphSage_accuracy_list_pubmed_batch_at_scale_sample_method_kmeans_cold_startFalse_redundancy10_pretrain_ratio0.01_ratio0.005_total_ratio0.08.npy')
Sage_batch_at_scale_random_08 = np.load('./pubmed/GraphSage_accuracy_list_pubmed_batch_at_scale_sample_method_random_choice_cold_startFalse_redundancy10_pretrain_ratio0.01_ratio0.005_total_ratio0.08.npy')
Sage_batch_at_scale_round_robin_08 = np.load('./pubmed/GraphSage_accuracy_list_pubmed_batch_at_scale_sample_method_round_robin_cold_startFalse_redundancy10_pretrain_ratio0.01_ratio0.005_total_ratio0.08.npy')
Sage_batch_at_scale_cluster_input_08 = np.load('./pubmed/GraphSage_accuracy_list_pubmed_batch_at_scale_sample_method_cluster_input_cold_startFalse_redundancy10_pretrain_ratio0.01_ratio0.005_total_ratio0.08.npy')
Sage_batch_at_scale_cluster_embed_08 = np.load('./pubmed/GraphSage_accuracy_list_pubmed_batch_at_scale_sample_method_cluster_embed_cold_startFalse_redundancy10_pretrain_ratio0.01_ratio0.005_total_ratio0.08.npy')
Sage_Grain_ball_08 = np.load('./pubmed/GraphSage_accuracy_list_pubmed_GRAIN_ball_ratio0.08.npy')
Sage_Grain_NND_08 = np.load('./pubmed/GraphSage_accuracy_list_pubmed_GRAIN_NND_ratio0.08.npy')


pubmed_random_acc_08 = np.max(Sage_random_08)
pubmed_degree_acc_08 = np.max(Sage_degree_08)
pubmed_laplacian_acc_08 = np.max(Sage_laplacian_08) 
pubmed_kmedoid_acc_08 = np.max(Sage_kmedoid_08)
pubmed_batch_at_scale_acc_08 = np.max(Sage_batch_at_scale_08)
pubmed_batch_at_scale_random_acc_08 = np.max(Sage_batch_at_scale_random_08)
pubmed_batch_at_scale_round_robin_acc_08 = np.max(Sage_batch_at_scale_round_robin_08)
pubmed_batch_at_scale_cluster_input_acc_08 = np.max(Sage_batch_at_scale_cluster_input_08)
pubmed_batch_at_scale_cluster_embed_acc_08 = np.max(Sage_batch_at_scale_cluster_embed_08)
pubmed_Grain_ball_acc_08 = np.max(Sage_Grain_ball_08)
pubmed_Grain_NND_acc_08 = np.max(Sage_Grain_NND_08)



Sage_256 = np.load('./pubmed/GraphSage_accuracy_list_pubmed.npy')
Sage_random_10 = np.load('./pubmed/GraphSage_accuracy_list_pubmed_random_ratio0.1.npy')
Sage_laplacian_10 = np.load('./pubmed/GraphSage_accuracy_list_pubmed_laplacian_ratio0.1.npy')
Sage_degree_10 = np.load('./pubmed/GraphSage_accuracy_list_pubmed_degree_ratio0.1.npy')
Sage_kmedoid_10 = np.load('./pubmed/GraphSage_accuracy_list_pubmed_Kmedoid_ncluster200_AggregateLayer3_ratio0.1.npy')
Sage_batch_at_scale_10 = np.load('./pubmed/GraphSage_accuracy_list_pubmed_batch_at_scale_pretrain_ratio0.01_ratio0.002_total_ratio0.1.npy')
Sage_batch_at_scale_random_10 = np.load('./pubmed/GraphSage_accuracy_list_pubmed_batch_at_scale_sample_method_random_choice_cold_startFalse_redundancy20_pretrain_ratio0.01_ratio0.002_total_ratio0.1.npy')
Sage_batch_at_scale_round_robin_10 = np.load('./pubmed/GraphSage_accuracy_list_pubmed_batch_at_scale_sample_method_round_robin_cold_startFalse_redundancy20_pretrain_ratio0.01_ratio0.002_total_ratio0.1.npy')
Sage_batch_at_scale_cluster_input_10 = np.load('./pubmed/GraphSage_accuracy_list_pubmed_batch_at_scale_sample_method_cluster_input_cold_startFalse_redundancy20_pretrain_ratio0.01_ratio0.002_total_ratio0.1.npy')
Sage_batch_at_scale_cluster_embed_10 = np.load('./pubmed/GraphSage_accuracy_list_pubmed_batch_at_scale_sample_method_cluster_embed_cold_startFalse_redundancy20_pretrain_ratio0.01_ratio0.002_total_ratio0.1.npy')
Sage_Grain_ball_10 = np.load('./pubmed/GraphSage_accuracy_list_pubmed_GRAIN_ball_ratio0.1.npy')
Sage_Grain_NND_10 = np.load('./pubmed/GraphSage_accuracy_list_pubmed_GRAIN_NND_ratio0.1.npy')
Sage_AGE_10 = np.load('./pubmed/GraphSage_accuracy_list_pubmed_AGE_ratio0.1.npy')
Sage_ANRMAB_10 = np.load('./pubmed/GraphSage_accuracy_list_pubmed_ANRMAB_ratio0.1.npy')


pubmed_acc_256 = np.max(Sage_256)
pubmed_random_acc_10 = np.max(Sage_random_10)
pubmed_laplacian_acc_10 = np.max(Sage_laplacian_10)
pubmed_degree_acc_10 = np.max(Sage_degree_10)
pubmed_kmedoid_acc_10 = np.max(Sage_kmedoid_10) 
pubmed_batch_at_scale_acc_10 = np.max(Sage_batch_at_scale_10)
pubmed_batch_at_scale_random_acc_10 = np.max(Sage_batch_at_scale_random_10)
pubmed_batch_at_scale_round_robin_acc_10 = np.max(Sage_batch_at_scale_round_robin_10)
pubmed_batch_at_scale_cluster_input_acc_10 = np.max(Sage_batch_at_scale_cluster_input_10)
pubmed_batch_at_scale_cluster_embed_acc_10 = np.max(Sage_batch_at_scale_cluster_embed_10)
pubmed_Grain_ball_acc_10 = np.max(Sage_Grain_ball_10)
pubmed_Grain_NND_acc_10 = np.max(Sage_Grain_NND_10)
pubmed_AGE_10 = np.max(Sage_AGE_10)
pubmed_ANRMAB_10 = np.max(Sage_ANRMAB_10)
ratio = np.array([0.03, 0.04, 0.05, 0.07, 0.10])

Random_acc = np.array([pubmed_random_acc_03, pubmed_random_acc_04, pubmed_random_acc_05, pubmed_random_acc_07, 
                       pubmed_random_acc_10])
Degree_acc = np.array([pubmed_degree_acc_03, pubmed_degree_acc_04, pubmed_degree_acc_05, pubmed_degree_acc_07, 
                       pubmed_degree_acc_10])
Laplacian_acc = np.array([pubmed_laplacian_acc_03, pubmed_laplacian_acc_04, pubmed_laplacian_acc_05, 
                          pubmed_laplacian_acc_07, pubmed_laplacian_acc_10])
FeatProp_acc = np.array([pubmed_kmedoid_acc_03, pubmed_kmedoid_acc_04, pubmed_kmedoid_acc_05, 
                         pubmed_kmedoid_acc_07, pubmed_kmedoid_acc_10])
Grain_ball_acc = np.array([pubmed_Grain_ball_acc_03, pubmed_Grain_ball_acc_04, pubmed_Grain_ball_acc_05, 
                           pubmed_Grain_ball_acc_07, pubmed_Grain_ball_acc_10])
Grain_NND_acc = np.array([pubmed_Grain_NND_acc_03, pubmed_Grain_NND_acc_04, pubmed_Grain_NND_acc_05, 
                          pubmed_Grain_NND_acc_07, pubmed_Grain_NND_acc_10])
GIDUS_acc = np.array([pubmed_batch_at_scale_acc_03, pubmed_batch_at_scale_acc_04, pubmed_batch_at_scale_acc_05, 
                      pubmed_batch_at_scale_acc_07, pubmed_batch_at_scale_acc_10])
AGE_acc = np.array([pubmed_AGE_03, pubmed_AGE_04, pubmed_AGE_05, pubmed_AGE_07, pubmed_AGE_10])
ANRMAB_acc = np.array([pubmed_ANRMAB_03, pubmed_ANRMAB_04, pubmed_ANRMAB_05, pubmed_ANRMAB_07, pubmed_ANRMAB_10])


index = [0, 1, 2, 3, 4]

plt.plot(ratio[index], GIDUS_acc[index], label="ScatterSample", marker = "o", markersize=5)
plt.plot(ratio[index], AGE_acc, label="AGE", marker = "o", markersize=5)
plt.plot(ratio[index], ANRMAB_acc, label="ANRMAB", marker = "o", markersize=5)
plt.plot(ratio[index], Random_acc[index], label="Random", marker = "o", markersize=5)
plt.plot(ratio[index], FeatProp_acc[index], label="FeatProp", marker = "o", markersize=5)
# plt.plot(ratio[index], Laplacian_acc[index], label="Laplacian", marker = "o", markersize=5)
plt.plot(ratio[index], Grain_ball_acc[index], label="Grain(ball-D)", marker = "o", markersize=5)
plt.plot(ratio[index], Grain_NND_acc[index], label="Grain(NND)", marker = "o", markersize=5)
plt.xlabel('Sampling Ratio', fontsize = 14)
plt.ylabel('Test Accuracy', fontsize = 14)
plt.xticks( fontsize=12)
plt.yticks(fontsize=12)
plt.legend()
plt.tight_layout()
plt.savefig("../Figure/pubmed_accuracy_comparison.png")
plt.show()
ratio = np.array([0.03, 0.04, 0.05, 0.07, 0.10])
GIDUS = np.array([pubmed_batch_at_scale_acc_03, pubmed_batch_at_scale_acc_04, pubmed_batch_at_scale_acc_05, 
                  pubmed_batch_at_scale_acc_07, pubmed_batch_at_scale_acc_10])

random = np.array([pubmed_batch_at_scale_random_acc_03, pubmed_batch_at_scale_random_acc_04, 
                   pubmed_batch_at_scale_random_acc_05, pubmed_batch_at_scale_random_acc_07, 
                   pubmed_batch_at_scale_random_acc_10])

round_robin = np.array([pubmed_batch_at_scale_round_robin_acc_03, pubmed_batch_at_scale_round_robin_acc_04, 
                  pubmed_batch_at_scale_round_robin_acc_05, pubmed_batch_at_scale_round_robin_acc_07, 
                  pubmed_batch_at_scale_round_robin_acc_10])

cluster_input = np.array([pubmed_batch_at_scale_cluster_input_acc_03, pubmed_batch_at_scale_cluster_input_acc_04, 
                          pubmed_batch_at_scale_cluster_input_acc_05, pubmed_batch_at_scale_cluster_input_acc_07, 
                          pubmed_batch_at_scale_cluster_input_acc_10])

cluster_embed = np.array([pubmed_batch_at_scale_cluster_embed_acc_03, pubmed_batch_at_scale_cluster_embed_acc_04, 
                          pubmed_batch_at_scale_cluster_embed_acc_05, pubmed_batch_at_scale_cluster_embed_acc_07, 
                          pubmed_batch_at_scale_cluster_embed_acc_10])

index = [0, 1, 2, 3, 4]

plt.plot(ratio[index], random[index], label="Random", marker = "o", markersize=5)
plt.plot(ratio[index], round_robin[index], label="Random Round Robin", marker = "o", markersize=5)
plt.plot(ratio[index], GIDUS[index], label="DiverseUncertainty", marker = "o", markersize=5)
plt.xlabel('Sampling Ratio', fontsize = 14)
plt.ylabel('Test Accuracy', fontsize = 14)
plt.xticks( fontsize=12)
plt.yticks(fontsize=12)
plt.legend(fontsize=13)
plt.tight_layout()
plt.savefig("../Figure/pubmed_compare_cluster_method.png")
plt.show()

plt.plot(ratio[index], cluster_embed[index], label="Cluster Input", marker = "o", markersize=5)
plt.plot(ratio[index], cluster_input[index], label="Cluster Embeddings", marker = "o", markersize=5)
plt.plot(ratio[index], GIDUS[index], label="Cluster Propagated Features", marker = "o", markersize=5)
plt.xlabel('Sampling Ratio', fontsize = 14)
plt.ylabel('Test Accuracy', fontsize = 14)
plt.xticks(fontsize=12)
plt.yticks(fontsize=12)
plt.legend(fontsize=13)
plt.tight_layout()
plt.savefig("../Figure/pubmed_compare_cluster_target.png")
plt.show()<jupyter_output><empty_output><jupyter_text>Evaluate sampling redundancy<jupyter_code>Sage_batch_at_scale_03_r1 = np.load('./pubmed/GraphSage_accuracy_list_pubmed_batch_at_scale_sample_method_kmeans_cold_startFalse_redundancy1_pretrain_ratio0.01_ratio0.005_total_ratio0.03.npy')
Sage_batch_at_scale_03_r5 = np.load('./pubmed/GraphSage_accuracy_list_pubmed_batch_at_scale_sample_method_kmeans_cold_startFalse_redundancy5_pretrain_ratio0.01_ratio0.005_total_ratio0.03.npy')
Sage_batch_at_scale_03_r10 = np.load('./pubmed/GraphSage_accuracy_list_pubmed_batch_at_scale_sample_method_kmeans_cold_startFalse_redundancy10_pretrain_ratio0.01_ratio0.005_total_ratio0.03.npy')
Sage_batch_at_scale_03_r20 = np.load('./pubmed/GraphSage_accuracy_list_pubmed_batch_at_scale_sample_method_kmeans_cold_startFalse_redundancy20_pretrain_ratio0.01_ratio0.005_total_ratio0.03.npy')
Sage_batch_at_scale_03_r40 = np.load('./pubmed/GraphSage_accuracy_list_pubmed_batch_at_scale_sample_method_kmeans_cold_startFalse_redundancy40_pretrain_ratio0.01_ratio0.005_total_ratio0.03.npy')

pubmed_03_r1 = np.max(Sage_batch_at_scale_03_r1)
pubmed_03_r5 = np.max(Sage_batch_at_scale_03_r5)
pubmed_03_r10 = np.max(Sage_batch_at_scale_03_r10)
pubmed_03_r20 = np.max(Sage_batch_at_scale_03_r20)
pubmed_03_r40 = np.max(Sage_batch_at_scale_03_r40)




Sage_batch_at_scale_05_r1 = np.load('./pubmed/GraphSage_accuracy_list_pubmed_batch_at_scale_sample_method_kmeans_cold_startFalse_redundancy1_pretrain_ratio0.01_ratio0.005_total_ratio0.05.npy')
Sage_batch_at_scale_05_r5 = np.load('./pubmed/GraphSage_accuracy_list_pubmed_batch_at_scale_sample_method_kmeans_cold_startFalse_redundancy5_pretrain_ratio0.01_ratio0.005_total_ratio0.05.npy')
Sage_batch_at_scale_05_r10 = np.load('./pubmed/GraphSage_accuracy_list_pubmed_batch_at_scale_sample_method_kmeans_cold_startFalse_redundancy20_pretrain_ratio0.01_ratio0.005_total_ratio0.05.npy')
Sage_batch_at_scale_05_r20 = np.load('./pubmed/GraphSage_accuracy_list_pubmed_batch_at_scale_sample_method_kmeans_cold_startFalse_redundancy20_pretrain_ratio0.01_ratio0.005_total_ratio0.05.npy')
Sage_batch_at_scale_05_r40 = np.load('./pubmed/GraphSage_accuracy_list_pubmed_batch_at_scale_sample_method_kmeans_cold_startFalse_redundancy40_pretrain_ratio0.01_ratio0.005_total_ratio0.05.npy')

pubmed_05_r1 = np.max(Sage_batch_at_scale_05_r1)
pubmed_05_r5 = np.max(Sage_batch_at_scale_05_r5)
pubmed_05_r10 = np.max(Sage_batch_at_scale_05_r10)
pubmed_05_r20 = np.max(Sage_batch_at_scale_05_r20)
pubmed_05_r40 = np.max(Sage_batch_at_scale_05_r40)




Sage_batch_at_scale_07_r1 = np.load('./pubmed/GraphSage_accuracy_list_pubmed_batch_at_scale_sample_method_kmeans_cold_startFalse_redundancy1_pretrain_ratio0.01_ratio0.005_total_ratio0.07.npy')
Sage_batch_at_scale_07_r5 = np.load('./pubmed/GraphSage_accuracy_list_pubmed_batch_at_scale_sample_method_kmeans_cold_startFalse_redundancy5_pretrain_ratio0.01_ratio0.005_total_ratio0.07.npy')
Sage_batch_at_scale_07_r10 = np.load('./pubmed/GraphSage_accuracy_list_pubmed_batch_at_scale_sample_method_kmeans_cold_startFalse_redundancy10_pretrain_ratio0.01_ratio0.005_total_ratio0.07.npy')
Sage_batch_at_scale_07_r20 = np.load('./pubmed/GraphSage_accuracy_list_pubmed_batch_at_scale_sample_method_kmeans_cold_startFalse_redundancy20_pretrain_ratio0.01_ratio0.005_total_ratio0.07.npy')
Sage_batch_at_scale_07_r40 = np.load('./pubmed/GraphSage_accuracy_list_pubmed_batch_at_scale_sample_method_kmeans_cold_startFalse_redundancy40_pretrain_ratio0.01_ratio0.005_total_ratio0.07.npy')


pubmed_07_r1 = np.max(Sage_batch_at_scale_07_r1)
pubmed_07_r5 = np.max(Sage_batch_at_scale_07_r5)
pubmed_07_r10 = np.max(Sage_batch_at_scale_07_r10)
pubmed_07_r20 = np.max(Sage_batch_at_scale_07_r20)
pubmed_07_r40 = np.max(Sage_batch_at_scale_07_r40)

Sage_batch_at_scale_10_r1 = np.load('./pubmed/GraphSage_accuracy_list_pubmed_batch_at_scale_sample_method_kmeans_cold_startFalse_redundancy1_pretrain_ratio0.01_ratio0.005_total_ratio0.1.npy')
Sage_batch_at_scale_10_r5 = np.load('./pubmed/GraphSage_accuracy_list_pubmed_batch_at_scale_sample_method_kmeans_cold_startFalse_redundancy5_pretrain_ratio0.01_ratio0.005_total_ratio0.1.npy')
Sage_batch_at_scale_10_r10 = np.load('./pubmed/GraphSage_accuracy_list_pubmed_batch_at_scale_sample_method_kmeans_cold_startFalse_redundancy10_pretrain_ratio0.01_ratio0.005_total_ratio0.1.npy')
Sage_batch_at_scale_10_r20 = np.load('./pubmed/GraphSage_accuracy_list_pubmed_batch_at_scale_sample_method_kmeans_cold_startFalse_redundancy20_pretrain_ratio0.01_ratio0.005_total_ratio0.1.npy')
Sage_batch_at_scale_10_r40 = np.load('./pubmed/GraphSage_accuracy_list_pubmed_batch_at_scale_sample_method_kmeans_cold_startFalse_redundancy40_pretrain_ratio0.01_ratio0.005_total_ratio0.1.npy')


pubmed_10_r1 = np.max(Sage_batch_at_scale_10_r1)
pubmed_10_r5 = np.max(Sage_batch_at_scale_10_r5)
pubmed_10_r10 = np.max(Sage_batch_at_scale_10_r10)
pubmed_10_r20 = np.max(Sage_batch_at_scale_10_r20)
pubmed_10_r40 = np.max(Sage_batch_at_scale_10_r40)
r_list = np.array([1, 5, 10, 20, 40])

pubmed_03 = np.array([pubmed_03_r1, pubmed_03_r5, pubmed_03_r10, pubmed_03_r20, pubmed_03_r40])
pubmed_05 = np.array([pubmed_05_r1, pubmed_05_r5, pubmed_05_r10, pubmed_05_r20, pubmed_05_r40])
pubmed_07 = np.array([pubmed_07_r1, pubmed_07_r5, pubmed_07_r10, pubmed_07_r20, pubmed_07_r40])
pubmed_10 = np.array([pubmed_10_r1, pubmed_10_r5, pubmed_10_r10, pubmed_10_r20, pubmed_10_r40])


index = [0, 1, 2, 3, 4]
plt.plot(r_list[index], pubmed_03[index], marker = "o", label="Sample Ratio = 3%")
plt.plot(r_list[index], pubmed_05[index], marker = "o", label="Sample Ratio = 5%")
plt.plot(r_list[index], pubmed_07[index], marker = "o", label="Sample Ratio = 7%")
plt.plot(r_list[index], pubmed_10[index], marker = "o", label="Sample Ratio = 10%")

plt.xlabel('Sampling Redundancy', fontsize = 14)
plt.ylabel('Test Accuracy', fontsize = 14)
plt.xticks(r_list, fontsize=12)
plt.yticks(fontsize=12)
plt.legend()
plt.savefig("../Figure/pubmed_sample_redundancy.png")
plt.show()
row_1 = np.array([pubmed_acc_256, pubmed_random_acc_01, pubmed_degree_acc_01, pubmed_laplacian_acc_01, 
                  pubmed_kmedoid_acc_01, pubmed_Grain_ball_acc_01, pubmed_Grain_NND_acc_01, pubmed_batch_at_scale_acc_01])
row_2 = np.array([pubmed_acc_256, pubmed_random_acc_05, pubmed_degree_acc_05, pubmed_laplacian_acc_05, 
                  pubmed_kmedoid_acc_05, pubmed_Grain_ball_acc_05, pubmed_Grain_NND_acc_05, pubmed_batch_at_scale_acc_05])
row_3 = np.array([pubmed_acc_256, pubmed_random_acc_10, pubmed_degree_acc_10, pubmed_laplacian_acc_10, 
                  pubmed_kmedoid_acc_10, pubmed_Grain_ball_acc_10, pubmed_Grain_NND_acc_10, pubmed_batch_at_scale_acc_10])

table = np.stack((row_1, row_2, row_3))
table = np.around(table, 3)
dataset = ["Pubmed, ratio=0.01", "Pubmed, ratio=0.05", "Pubmed, ratio=0.1"]
names = ["Standard", "Random", "Degree", "Laplacian", "FeatProp (2-layer)", "Grain_ball", "Grain_NND", "Uncertain+Cluster"]

df = pd.DataFrame(table, columns=names, index = dataset)
df.round(3)
display(df)



row_1 = np.array([pubmed_batch_at_scale_acc_01, pubmed_batch_at_scale_random_acc_01, 
                  pubmed_batch_at_scale_round_robin_acc_01, pubmed_batch_at_scale_cluster_input_acc_01,
                 pubmed_batch_at_scale_cluster_embed_acc_01])
row_2 = np.array([pubmed_batch_at_scale_acc_05, pubmed_batch_at_scale_random_acc_05, 
                  pubmed_batch_at_scale_round_robin_acc_05, pubmed_batch_at_scale_cluster_input_acc_05,
                 pubmed_batch_at_scale_cluster_embed_acc_05])
row_3 = np.array([pubmed_batch_at_scale_acc_10, pubmed_batch_at_scale_random_acc_10, 
                  pubmed_batch_at_scale_round_robin_acc_10, pubmed_batch_at_scale_cluster_input_acc_10,
                 pubmed_batch_at_scale_cluster_embed_acc_10])

table = np.stack((row_1, row_2, row_3))
table = np.around(table, 3)
dataset = ["pubmed, ratio=0.03", "pubmed, ratio=0.05", "pubmed, ratio=0.1"]
names = ["GIDUS (kmeans++)", "GIDUS (random)", "GIDUS (round robin)", "GIDUS (cluster input)", "GIDUS (cluster embed)"]

df = pd.DataFrame(table, columns=names, index = dataset)
df.round(3)
display(df)<jupyter_output><empty_output>
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