Node Classification On Squirrel

Metrics

Accuracy

Results

Performance results of various models on this benchmark

		Paper Title	Repository
FaberNet	76.71±1.92	HoloNets: Spectral Convolutions do extend to Directed Graphs
CoED	75.32±1.82	Improving Graph Neural Networks by Learning Continuous Edge Directions
Dir-GNN	75.31±1.92	Edge Directionality Improves Learning on Heterophilic Graphs
HLP Concat	74.17±1.83	Simple Truncated SVD based Model for Node Classification on Heterophilic Graphs	-
FSGNN (8-hop)	74.10±1.89	Improving Graph Neural Networks with Simple Architecture Design
DJ-GNN	73.48±1.59	Diffusion-Jump GNNs: Homophiliation via Learnable Metric Filters
H2GCN+DHGR	72.24±1.52	Make Heterophily Graphs Better Fit GNN: A Graph Rewiring Approach	-
Graph ESN	71.2±1.5	Beyond Homophily with Graph Echo State Networks	-
SADE-GCN	68.20±1.57	Self-attention Dual Embedding for Graphs with Heterophily	-
ACMII-GCN++	67.4 ± 2.21	Revisiting Heterophily For Graph Neural Networks
ACMII-GCN+	67.07 ± 1.65	Revisiting Heterophily For Graph Neural Networks
ACM-GCN++	67.06 ± 1.66	Revisiting Heterophily For Graph Neural Networks
ACM-GCN+	66.98 ± 1.71	Revisiting Heterophily For Graph Neural Networks
UGT	66.96 ±2.49	Transitivity-Preserving Graph Representation Learning for Bridging Local Connectivity and Role-based Similarity
RDGNN-I	65.62 ± 2.33	Graph Neural Reaction Diffusion Models	-
CNMPGNN	63.60±1.96	CN-Motifs Perceptive Graph Neural Networks	-
M2M-GNN	63.60 ± 1.7	Sign is Not a Remedy: Multiset-to-Multiset Message Passing for Learning on Heterophilic Graphs
LW-GCN	62.6±1.6	Label-Wise Graph Convolutional Network for Heterophilic Graphs
Ordered GNN	62.44±1.96	Ordered GNN: Ordering Message Passing to Deal with Heterophily and Over-smoothing
HDP	62.07 ± 1.57	Heterophilous Distribution Propagation for Graph Neural Networks	-

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Node Classification On Squirrel

Metrics

Results