Node Classification on Cornell

60개 결과 · ⬇ CSV · JSON

Accuracy

Rank	Model	Accuracy	Accuracy (%)	Paper	Code	Year
1	RDGNN-I	92.72 ± 5.88	–	Graph Neural Reaction Diffusion Models		2024
2	CATv3-sup	88.8±2.1	–	CAT: A Causally Graph Attention Network for Trimming Heterophilic Graph	geox-lab/cat	2023
3	FSGNN (8-hop)	87.84±6.19	–	Improving Graph Neural Networks with Simple Architecture Design	sunilkmaurya/FSGNN	2021
4	H2GCN-RARE (λ=1.0)	87.84±4.05	–	GraphRARE: Reinforcement Learning Enhanced Graph Neural Network with Relative Entropy		2023
5	Ordered GNN	87.03±4.73	–	Ordered GNN: Ordering Message Passing to Deal with Heterophily and Over-smoothing	lumia-group/orderedgnn	2023
6	DJ-GNN	87.03±1.62	–	Diffusion-Jump GNNs: Homophiliation via Learnable Metric Filters	AhmedBegggaUA/TFM	2023
7	UniG-Encoder	86.75±6.56	–	UniG-Encoder: A Universal Feature Encoder for Graph and Hypergraph Node Classification	minhzou/unig-encoder	2023
8	Diag-NSD	86.49 ± 7.35	–	Neural Sheaf Diffusion: A Topological Perspective on Heterophily and Oversmoothing in GNNs	twitter-research/neural-sheaf-diffusion	2022
9	ACMII-GCN++	86.49 ± 6.73	–	Revisiting Heterophily For Graph Neural Networks	SitaoLuan/ACM-GNN	2022
10	GCNH	86.49±6.98	–	GCNH: A Simple Method For Representation Learning On Heterophilous Graphs	smartdata-polito/gcnh	2023
11	M2M-GNN	86.48 ± 6.1	–	Sign is Not a Remedy: Multiset-to-Multiset Message Passing for Learning on Heterophilic Graphs	Jinx-byebye/m2mgnn	2024
12	SADE-GCN	86.21±5.59	–	Self-attention Dual Embedding for Graphs with Heterophily		2023
13	LHS	85.96±5.1	–	Refining Latent Homophilic Structures over Heterophilic Graphs for Robust Graph Convolution Networks		2023
14	GloGNN++	85.95±5.10	–	Finding Global Homophily in Graph Neural Networks When Meeting Heterophily	recklessronan/glognn	2022
15	Conn-NSD	85.95±7.72	–	Sheaf Neural Networks with Connection Laplacians	antoniopurificato/sheaf4rec	2022
16	ACMII-GCN	85.95 ± 5.64	–	Revisiting Heterophily For Graph Neural Networks	SitaoLuan/ACM-GNN	2022
17	GGCN	85.68 ± 6.63	–	Two Sides of the Same Coin: Heterophily and Oversmoothing in Graph Convolutional Neural Networks	yujun-yan/heterophily_and_oversmoothing	2021
18	Gen-NSD	85.68 ± 6.51	–	Neural Sheaf Diffusion: A Topological Perspective on Heterophily and Oversmoothing in GNNs	twitter-research/neural-sheaf-diffusion	2022
19	ACM-GCN+	85.68 ± 4.84	–	Revisiting Heterophily For Graph Neural Networks	SitaoLuan/ACM-GNN	2022
20	ACM-GCN++	85.68 ± 5.8	–	Revisiting Heterophily For Graph Neural Networks	SitaoLuan/ACM-GNN	2022