Node Classification On Actor

Metrics

Accuracy

Results

Performance results of various models on this benchmark

Model Name	Accuracy	Paper Title	Repository
CATv3-sup	38.5±1.2	CAT: A Causally Graph Attention Network for Trimming Heterophilic Graph	-
MbaGCN	37.97±0.91	Mamba-Based Graph Convolutional Networks: Tackling Over-smoothing with Selective State Space	-
GCNH	36.89 ± 1.50	GCNH: A Simple Method For Representation Learning On Heterophilous Graphs	-
ACM-GCN+	36.26 ± 1.34	Revisiting Heterophily For Graph Neural Networks	-
Gen-NSD	37.80 ± 1.22	Neural Sheaf Diffusion: A Topological Perspective on Heterophily and Oversmoothing in GNNs	-
NLMLP	37.9 ± 1.3	Non-Local Graph Neural Networks	-
FAGCN	34.82 ± 1.35	Beyond Low-frequency Information in Graph Convolutional Networks	-
SADE-GCN	37.91 ± 0.97	Self-attention Dual Embedding for Graphs with Heterophily	-
TE-GCNN	-	Transfer Entropy in Graph Convolutional Neural Networks	-
HiGNN	37.21 ± 1.35	Learn from Heterophily: Heterophilous Information-enhanced Graph Neural Network	-
GPRGNN+DHGR	37.43 ± 0.78	Make Heterophily Graphs Better Fit GNN: A Graph Rewiring Approach	-
SignGT	38.65±0.32	SignGT: Signed Attention-based Graph Transformer for Graph Representation Learning	-
GPRGCN	35.16 ± 0.9	Adaptive Universal Generalized PageRank Graph Neural Network	-
RDGNN-I	38.69 ± 1.41	Graph Neural Reaction Diffusion Models	-
ACMII-GCN	36.31 ± 1.2	Revisiting Heterophily For Graph Neural Networks	-
ACMII-GCN++	37.09 ± 1.32	Revisiting Heterophily For Graph Neural Networks	-
M2M-GNN	36.72 ± 1.6	Sign is Not a Remedy: Multiset-to-Multiset Message Passing for Learning on Heterophilic Graphs	-
Diag-NSD	37.79 ± 1.01	Neural Sheaf Diffusion: A Topological Perspective on Heterophily and Oversmoothing in GNNs	-
HDP	37.26 ± 0.67	Heterophilous Distribution Propagation for Graph Neural Networks	-
NCGCN	43.16 ± 1.32	Clarify Confused Nodes via Separated Learning	-

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