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SOTA
Node Classification
Node Classification On Chameleon 60 20 20
Node Classification On Chameleon 60 20 20
评估指标
1:1 Accuracy
评测结果
各个模型在此基准测试上的表现结果
Columns
模型名称
1:1 Accuracy
Paper Title
Repository
GAT
63.9 ± 0.46
Graph Attention Networks
ACM-GCNII*
61.66 ± 2.29
Revisiting Heterophily For Graph Neural Networks
GCN
64.18 ± 2.62
Semi-Supervised Classification with Graph Convolutional Networks
ACMII-Snowball-3
67.53 ± 2.83
Revisiting Heterophily For Graph Neural Networks
Geom-GCN*
60.9
Geom-GCN: Geometric Graph Convolutional Networks
GCN+JK
64.68 ± 2.85
Revisiting Heterophily For Graph Neural Networks
ACMII-GCN++
75.93 ± 1.71
Revisiting Heterophily For Graph Neural Networks
ACM-GCNII
58.73 ± 2.52
Revisiting Heterophily For Graph Neural Networks
ACM-Snowball-2
68.51 ± 1.7
Revisiting Heterophily For Graph Neural Networks
ACMII-Snowball-2
67.83 ± 2.63
Revisiting Heterophily For Graph Neural Networks
GraphSAGE
62.15 ± 0.42
Inductive Representation Learning on Large Graphs
Snowball-3
65.49 ± 1.64
Break the Ceiling: Stronger Multi-scale Deep Graph Convolutional Networks
MLP-2
46.72 ± 0.46
Revisiting Heterophily For Graph Neural Networks
Snowball-2
64.99 ± 2.39
Break the Ceiling: Stronger Multi-scale Deep Graph Convolutional Networks
GCNII*
62.8 ± 2.87
Simple and Deep Graph Convolutional Networks
ACMII-GCN+
75.51 ± 1.58
Revisiting Heterophily For Graph Neural Networks
MixHop
36.28 ± 10.22
MixHop: Higher-Order Graph Convolutional Architectures via Sparsified Neighborhood Mixing
HH-GraphSAGE
62.98 ± 3.35
Half-Hop: A graph upsampling approach for slowing down message passing
ACM-Snowball-3
68.4 ± 2.05
Revisiting Heterophily For Graph Neural Networks
ACM-SGC-1
63.68 ± 1.62
Revisiting Heterophily For Graph Neural Networks
0 of 38 row(s) selected.
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