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Node Classification On Squirrel 60 20 20

Metrics

1:1 Accuracy

Results

Performance results of various models on this benchmark

Paper Title
GNNDLD77.72±0.84 GNNDLD: Graph Neural Network with Directional Label Distribution
ACMII-GCN++69.98 ± 1.53Revisiting Heterophily For Graph Neural Networks
ACMII-GCN+69.81 ± 1.11Revisiting Heterophily For Graph Neural Networks
ACM-GCN+69.26 ± 1.11Revisiting Heterophily For Graph Neural Networks
ACM-GCN++68.56 ± 1.33Revisiting Heterophily For Graph Neural Networks
NFGNN58.9±0.35Node-oriented Spectral Filtering for Graph Neural Networks
ACM-Snowball-255.97 ± 2.03Revisiting Heterophily For Graph Neural Networks
ACM-Snowball-355.73 ± 2.39Revisiting Heterophily For Graph Neural Networks
ACMII-GCN54.53 ± 2.09Revisiting Heterophily For Graph Neural Networks
ACMII-Snowball-253.48 ± 0.6Revisiting Heterophily For Graph Neural Networks
GCN+JK53.40 ± 1.90Revisiting Heterophily For Graph Neural Networks
ACMII-Snowball-352.31 ± 1.57Revisiting Heterophily For Graph Neural Networks
 GAT+JK52.28 ± 3.61Revisiting Heterophily For Graph Neural Networks
BernNet51.35 ± 0.73BernNet: Learning Arbitrary Graph Spectral Filters via Bernstein Approximation
GPRGNN49.93 ± 0.53Adaptive Universal Generalized PageRank Graph Neural Network
Snowball-348.25 ± 0.94Break the Ceiling: Stronger Multi-scale Deep Graph Convolutional Networks
Snowball-247.88 ± 1.23Break the Ceiling: Stronger Multi-scale Deep Graph Convolutional Networks
SGC-147.62 ± 1.27Simplifying Graph Convolutional Networks
HH-GCN47.19 ± 1.21Half-Hop: A graph upsampling approach for slowing down message passing
ACM-SGC-146.4 ± 1.13Revisiting Heterophily For Graph Neural Networks
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Node Classification On Squirrel 60 20 20 | SOTA | HyperAI