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Node Classification On Cora 60 20 20 Random

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1:1 Accuracy

Ergebnisse

Leistungsergebnisse verschiedener Modelle zu diesem Benchmark

Paper Title
GNNDLD92.99 ±0.9GNNDLD: Graph Neural Network with Directional Label Distribution
ACM-GCN+89.75 ± 1.16Revisiting Heterophily For Graph Neural Networks
ACM-Snowball-389.59 ± 1.58Revisiting Heterophily For Graph Neural Networks
GAT+JK89.52 ± 0.43Revisiting Heterophily For Graph Neural Networks
ACMII-GCN++89.47 ± 1.08Revisiting Heterophily For Graph Neural Networks
ACMII-Snowball-389.36 ± 1.26Revisiting Heterophily For Graph Neural Networks
ACM-GCN++89.33 ± 0.81Revisiting Heterophily For Graph Neural Networks
Snowball-389.33 ± 1.3Break the Ceiling: Stronger Multi-scale Deep Graph Convolutional Networks
ACMII-GCN+89.18 ± 1.11Revisiting Heterophily For Graph Neural Networks
ACM-GCNII89.1 ± 1.61Revisiting Heterophily For Graph Neural Networks
ACM-GCNII*89.00 ± 1.35Revisiting Heterophily For Graph Neural Networks
ACMII-GCN89.00 ± 0.72Revisiting Heterophily For Graph Neural Networks
GCNII88.98 ± 1.33Simple and Deep Graph Convolutional Networks
ACMII-Snowball-288.95 ± 1.04Revisiting Heterophily For Graph Neural Networks
GCNII*88.93 ± 1.37Simple and Deep Graph Convolutional Networks
FAGCN88.85 ± 1.36Beyond Low-frequency Information in Graph Convolutional Networks
ACM-Snowball-288.83 ± 1.49Revisiting Heterophily For Graph Neural Networks
Snowball-288.64 ± 1.15Break the Ceiling: Stronger Multi-scale Deep Graph Convolutional Networks
BernNet88.52 ± 0.95BernNet: Learning Arbitrary Graph Spectral Filters via Bernstein Approximation
GCN87.78 ± 0.96Semi-Supervised Classification with Graph Convolutional Networks
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