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ResearchFormerly WizWand

Node Classification on DBLP (Micro/Macro F1)

98.83Micro-F1NodeSketch
75.398881.481987.56593.6481Dec 17, 2019Dec 26, 2020Jan 5, 2022Jan 15, 2023Jan 25, 2024Feb 3, 2025Feb 13, 2026
Updated 5mo ago

Evaluation results

MethodLinks
NodeSketchParameter Tuning Strategy=Tuned for each dataset2019.1298.8398.81
NetHashParameter Tuning Strategy=Tuned for each dataset2019.1297.6197.57
Oraclesetting=unsupervised graph domain adaptation2025.0197.396.9
Oraclesetting=unsupervised graph domain adaptation2025.0196.996.8
LMSPSmeta-path selection=searched2023.0795.6695.35
RpHGNNLearning Style=Pre-computation2023.1095.5595.23
SlotGAT2024.0595.3194.95
SlotGAT2023.0795.3194.95
SeHGNNLearning Style=Pre-computation2023.1095.2494.86
SeHGNN2023.0795.2494.86
PMMM2023.0795.1494.82
HINormerLearning Style=E2E2023.1094.9494.57
HINormer2023.0794.9494.57
SCHOOL2024.1294.794
space4HGNN2024.0594.6394.24
space4HGNN2023.0794.6394.24
NARSLearning Style=Pre-computation2023.1094.6194.18
NARS2023.0794.6194.18
simpleHGN2024.0594.4694.01
Simple-HGNLearning Style=E2E2023.1094.4694.01
Simple-HGN2023.0794.4694.01
GraphMSE2023.0794.4494.08
HERO2024.1294.493.8
DiffMG2023.0794.294.01
DisenHAN2024.0594.1893.66
GTN2024.0593.9793.52
GTNLearning Style=E2E2023.1093.9793.52
GTN2023.0793.9793.52
LMSPSmeta-path selection=random2023.0793.8493.57
GAT2023.0793.8393.39
RSHN2024.0593.8193.34
RSHNLearning Style=E2E2023.1093.8193.34
RSHN2023.0793.8193.34
CPIM2024.1293.893.2
MAGNN2024.0593.7693.28
MAGNNLearning Style=E2E2023.1093.7693.28
MAGNN2023.0793.7693.28
NetMFParameter Tuning Strategy=Tuned for each dataset2019.1293.5993.46
HGT2024.0593.4993.01
HGTLearning Style=E2E2023.1093.4993.01
HGT2023.0793.4993.01
HGMAE2024.1293.492.9
GAT2024.0593.3993.83
GATLearning Style=E2E2023.1093.3993.83
INH-MF2019.1293.2793.16
Node2VecParameter Tuning Strategy=Tuned for each dataset2019.1293.2593.12
HGCML2024.1293.291.9
GraphSAGELearning Style=E2E2023.1093.0692.53
DMGI2024.1292.992.1
VERSEParameter Tuning Strategy=Tuned once for all datasets2019.1292.7992.66
HARP-DWalkMeta technique=HARP2019.1292.6692.53
HetGNN2024.0592.3391.76
HetGNNLearning Style=E2E2023.1092.3391.76
HetGNN2023.0792.3391.76
HDMI2024.1292.291.3
GAT2024.1292.191
RGCN2024.0592.0791.52
RGCNLearning Style=E2E2023.1092.0791.52
RGCN2023.0792.0791.52
HAN2024.0592.0591.67
HANLearning Style=E2E2023.1092.0591.67
HAN2023.0792.0591.67
HAN2024.129291.2
DMGIattnattention=true2024.1291.890.9
HGT2024.1291.790.9
HeCo2024.1291.691
HOPEParameter Tuning Strategy=Tuned for each dataset2019.1291.4791.3
GCN2024.0591.4790.84
GCNLearning Style=E2E2023.1091.4790.84
GCN2023.0791.4790.84
GraRepParameter Tuning Strategy=Tuned for each dataset2019.1291.4191.25
GCN2024.1290.990.2
GPH2Labels=52026.0290.3689.66
DeepWalk2024.1289.588.1
DAOCParameter Tuning Strategy=Parameter-free2019.1289.2489.03
GPH2Labels=32026.0288.3487.61
HEROLabels=52026.0288.1487.49
HARP-LINEMeta technique=HARP2019.1288.0787.84
HGPromptLabels=52026.0288.0787.05
DAORParameter Tuning Strategy=Parameter-free2019.1287.8687.64
HEROLabels=32026.0287.6686.91
Mp2vec2024.1287.685.7
HGPromptLabels=32026.0287.2786.03
LINE2019.1286.8386.54
HGMAELabels=52026.0285.5484.9
HeCoLabels=52026.0283.8683.31
DeepWalkParameter Tuning Strategy=Tuned once for all datasets2019.1283.4683.16
HGMAELabels=32026.0282.1680.71
GGDA-GCNsetting=unsupervised graph domain adaptation2025.0181.680.7
GGDA-GATsetting=unsupervised graph domain adaptation2025.0181.580.3
HetSANN2024.0580.5678.55
HetSANNLearning Style=E2E2023.1080.5678.55
HetSANN2023.0780.5678.55
TDSSsetting=unsupervised graph domain adaptation2025.0180.277.4
ACDNEsetting=unsupervised graph domain adaptation2025.0179.277.1
ASNsetting=unsupervised graph domain adaptation2025.0178.174.1
HeCoLabels=32026.0277.5576.07
GGDA-GATsetting=unsupervised graph domain adaptation2025.0177.475.9
A2GNNsetting=unsupervised graph domain adaptation2025.0176.577.8
GGDA-SAGEsetting=unsupervised graph domain adaptation2025.0176.376