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GraphData Augmentation
MethodConsideredPartTypePerturbed Part
DropEdgeASamplingA
GAUG-OA&XReconstruction
FLAGXNoise Injection
G-GCNA&XReconstructionAXX
Local AugmentationA&XGenerationX
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Algorithm 1 The framework to train the Generator Q and the downstream GNN Pθ using the initial feature matrix X and the generated feature matrix X selected by the acquisition function
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MethodCoraCiteseerPubmed
Chebyshev (Defferrard et al.,2016)81.269.874.4
APPNP (Klicpera et al.,2019)83.871.679.7
MixHop (Abu-El-Haija et al.,2019)81.971.480.8
Graph U-net (Gao& Ji,2019)84.473.279.6
GSNN-M (Wang et al.,2020a)83.972.279.1
S²GC (Zhu & Koniusz,2021)83.573.680.2
GCN (Kipf & Welling,2017)81.670.378.9
G-GCN (Zhu et al.,2020)83.771.380.9
DropEdge-GCN (Rong et al.,2020)82.872.379.6
GAUG-O-GCN (Zhao et al.,2021)83.673.379.3
LA-GCN84.172.581.3
GAT (Velickovic et al., 2018)83.070.40OM
LA-GAT83.972.3OOM
GCNII (Chen et al.,2020) LA-GCNII85.273.180.0
85.273.781.6
GRAND (Feng et al.,2020)85.475.482.7
LA-GRAND85.875.883.3
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MethodSquirrelActorChameleonCornell
APPNP21.632.133.058.7
S²GC21.327.830.257.2
GCN22.526.225.155.7
DropEdge-GCN21.926.525.053.6
LA-GCN23.227.028.956.1
GAT24.227.234.855.8
LA-GAT28.227.438.656.5
GCNII25.331.930.257.3
LA-GCNII28.632.732.556.6
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GCNII + plain neighborhood83.371.978.1
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Mask Ratio0.10.20.40.80.10.20.40.80.10.20.40.8
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LA-GCN83.5 (↓0.6)83.1(↓1.0)81.6(↓2.5)81.1 (↓3.0)72.2(↓0.3)71.7 (↓0.8)69.3 (↓3.2)65.9 (↓6.6)81.4(↓0.1)80.9 (↓0.6)80.5 (↓1.0)79.4 (↓2.1)
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