Node Classification On Wisconsin

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Accuracy

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이 벤치마크에서 각 모델의 성능 결과

		Paper Title
5-HiGCN	94.99±0.65	Higher-order Graph Convolutional Network with Flower-Petals Laplacians on Simplicial Complexes
RDGNN-I	93.72 ± 4.59	Graph Neural Reaction Diffusion Models
H2GCN-RARE (λ=1.0)	90.00±2.97	GraphRARE: Reinforcement Learning Enhanced Graph Neural Network with Relative Entropy
O(d)-NSD	89.41 ± 4.74	Neural Sheaf Diffusion: A Topological Perspective on Heterophily and Oversmoothing in GNNs
Gen-NSD	89.21 ± 3.84	Neural Sheaf Diffusion: A Topological Perspective on Heterophily and Oversmoothing in GNNs
M2M-GNN	89.01 ± 4.1	Sign is Not a Remedy: Multiset-to-Multiset Message Passing for Learning on Heterophilic Graphs
HDP	88.82 ± 3.40	Heterophilous Distribution Propagation for Graph Neural Networks
MGNN + Hetero-S (6 layers)	88.77	The Heterophilic Snowflake Hypothesis: Training and Empowering GNNs for Heterophilic Graphs
Conn-NSD	88.73±4.47	Sheaf Neural Networks with Connection Laplacians
Diag-NSD	88.63 ± 2.75	Neural Sheaf Diffusion: A Topological Perspective on Heterophily and Oversmoothing in GNNs
SADE-GCN	88.63±4.54	Self-attention Dual Embedding for Graphs with Heterophily
ACM-GCN+	88.43 ± 2.39	Revisiting Heterophily For Graph Neural Networks
FSGNN (3-hop)	88.43±3.22	Improving Graph Neural Networks with Simple Architecture Design
ACM-GCN	88.43 ± 3.22	Revisiting Heterophily For Graph Neural Networks
ACMII-GCN++	88.43 ± 3.66	Revisiting Heterophily For Graph Neural Networks
LHS	88.32±2.3	Refining Latent Homophilic Structures over Heterophilic Graphs for Robust Graph Convolution Networks
ACM-GCN++	88.24 ± 3.16	Revisiting Heterophily For Graph Neural Networks
GloGNN++	88.04±3.22	Finding Global Homophily in Graph Neural Networks When Meeting Heterophily
ACMII-GCN+	88.04 ± 3.66	Revisiting Heterophily For Graph Neural Networks
Ordered GNN	88.04±3.63	Ordered GNN: Ordering Message Passing to Deal with Heterophily and Over-smoothing

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Node Classification On Wisconsin

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평가 결과