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Node Classification On Wisconsin

Metrics

Accuracy

Results

Performance results of various models on this benchmark

Paper Title
5-HiGCN94.99±0.65Higher-order Graph Convolutional Network with Flower-Petals Laplacians on Simplicial Complexes
RDGNN-I93.72 ± 4.59Graph Neural Reaction Diffusion Models
H2GCN-RARE (λ=1.0)90.00±2.97GraphRARE: Reinforcement Learning Enhanced Graph Neural Network with Relative Entropy
O(d)-NSD89.41 ± 4.74Neural Sheaf Diffusion: A Topological Perspective on Heterophily and Oversmoothing in GNNs
Gen-NSD89.21 ± 3.84Neural Sheaf Diffusion: A Topological Perspective on Heterophily and Oversmoothing in GNNs
M2M-GNN89.01 ± 4.1Sign is Not a Remedy: Multiset-to-Multiset Message Passing for Learning on Heterophilic Graphs
HDP88.82 ± 3.40Heterophilous Distribution Propagation for Graph Neural Networks
MGNN + Hetero-S (6 layers)88.77The Heterophilic Snowflake Hypothesis: Training and Empowering GNNs for Heterophilic Graphs
Conn-NSD88.73±4.47Sheaf Neural Networks with Connection Laplacians
Diag-NSD88.63 ± 2.75Neural Sheaf Diffusion: A Topological Perspective on Heterophily and Oversmoothing in GNNs
SADE-GCN88.63±4.54Self-attention Dual Embedding for Graphs with Heterophily
ACM-GCN+88.43 ± 2.39Revisiting Heterophily For Graph Neural Networks
FSGNN (3-hop)88.43±3.22Improving Graph Neural Networks with Simple Architecture Design
ACM-GCN88.43 ± 3.22Revisiting Heterophily For Graph Neural Networks
ACMII-GCN++88.43 ± 3.66Revisiting Heterophily For Graph Neural Networks
LHS88.32±2.3Refining Latent Homophilic Structures over Heterophilic Graphs for Robust Graph Convolution Networks
ACM-GCN++88.24 ± 3.16Revisiting Heterophily For Graph Neural Networks
GloGNN++88.04±3.22Finding Global Homophily in Graph Neural Networks When Meeting Heterophily
ACMII-GCN+88.04 ± 3.66Revisiting Heterophily For Graph Neural Networks
Ordered GNN88.04±3.63Ordered GNN: Ordering Message Passing to Deal with Heterophily and Over-smoothing
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