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Node Classification
Node Classification On Cornell
Node Classification On Cornell
Metrics
Accuracy
Results
Performance results of various models on this benchmark
Columns
Model Name
Accuracy
Paper Title
Repository
CT-Layer
69.04
DiffWire: Inductive Graph Rewiring via the Lovász Bound
ACM-GCN++
85.68 ± 5.8
Revisiting Heterophily For Graph Neural Networks
RDGNN-I
92.72 ± 5.88
Graph Neural Reaction Diffusion Models
-
Gen-NSD
85.68 ± 6.51
Neural Sheaf Diffusion: A Topological Perspective on Heterophily and Oversmoothing in GNNs
SADE-GCN
86.21±5.59
Self-attention Dual Embedding for Graphs with Heterophily
-
Geom-GCN-I
56.76
Geom-GCN: Geometric Graph Convolutional Networks
GREET+CausalMP
68.23±2.90
Heterophilic Graph Neural Networks Optimization with Causal Message-passing
-
UniG-Encoder
86.75±6.56
UniG-Encoder: A Universal Feature Encoder for Graph and Hypergraph Node Classification
Geom-GCN-S
55.68
Geom-GCN: Geometric Graph Convolutional Networks
PathNet
-
Beyond Homophily: Structure-aware Path Aggregation Graph Neural Network
GloGNN
83.51±4.26
Finding Global Homophily in Graph Neural Networks When Meeting Heterophily
H2GCN-1
78.11 ± 6.68
Beyond Homophily in Graph Neural Networks: Current Limitations and Effective Designs
ACM-SGC-1
82.43 ± 5.44
Revisiting Heterophily For Graph Neural Networks
GPRGCN
78.11 ± 6.55
Adaptive Universal Generalized PageRank Graph Neural Network
DeltaGNN - control + DC
75.67±1.91
DeltaGNN: Graph Neural Network with Information Flow Control
ACM-SGC-2
82.43 ± 5.44
Revisiting Heterophily For Graph Neural Networks
ACMII-GCN
85.95 ± 5.64
Revisiting Heterophily For Graph Neural Networks
GRADE-GAT
83.3±7.0
Graph Neural Aggregation-diffusion with Metastability
-
CNMPGNN
82.38 ± 6.13
CN-Motifs Perceptive Graph Neural Networks
-
ACMII-GCN++
86.49 ± 6.73
Revisiting Heterophily For Graph Neural Networks
0 of 60 row(s) selected.
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