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Node Classification On Citeseer 60 20 20

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1:1 Accuracy

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Leistungsergebnisse verschiedener Modelle zu diesem Benchmark

Paper Title
GNNDLD86.3±1.24GNNDLD: Graph Neural Network with Directional Label Distribution
FAGCN82.37 ± 1.46Beyond Low-frequency Information in Graph Convolutional Networks
ACM-GCNII82.28 ± 1.12Revisiting Heterophily For Graph Neural Networks
ACMII-Snowball-282.07 ± 1.04Revisiting Heterophily For Graph Neural Networks
ACMII-GCN+81.87 ± 1.38Revisiting Heterophily For Graph Neural Networks
ACM-GCN++81.83 ± 1.65Revisiting Heterophily For Graph Neural Networks
GCNII*81.83 ± 1.78Simple and Deep Graph Convolutional Networks
ACMII-GCN81.79 ± 0.95Revisiting Heterophily For Graph Neural Networks
ACMII-GCN++81.76 ± 1.25Revisiting Heterophily For Graph Neural Networks
ACM-GCNII*81.69 ± 1.25Revisiting Heterophily For Graph Neural Networks
ACM-GCN+81.65 ± 1.48Revisiting Heterophily For Graph Neural Networks
GCNII81.58 ± 1.3Simple and Deep Graph Convolutional Networks
ACM-Snowball-281.58 ± 1.23Revisiting Heterophily For Graph Neural Networks
ACMII-Snowball-381.56 ± 1.15Revisiting Heterophily For Graph Neural Networks
Snowball-281.53 ± 1.71Break the Ceiling: Stronger Multi-scale Deep Graph Convolutional Networks
GCN81.39 ± 1.23Semi-Supervised Classification with Graph Convolutional Networks
ACM-Snowball-381.32 ± 0.97Revisiting Heterophily For Graph Neural Networks
ACM-SGC-180.96 ± 0.93Revisiting Heterophily For Graph Neural Networks
ACM-SGC-280.93 ± 1.16Revisiting Heterophily For Graph Neural Networks
Snowball-380.93 ± 1.32Break the Ceiling: Stronger Multi-scale Deep Graph Convolutional Networks
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Node Classification On Citeseer 60 20 20 | SOTA | HyperAI