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플랫폼
홈
SOTA
그래프 회귀
Graph Regression On Zinc
Graph Regression On Zinc
평가 지표
MAE
평가 결과
이 벤치마크에서 각 모델의 성능 결과
Columns
모델 이름
MAE
Paper Title
Graph-JEPA
0.434
Graph-level Representation Learning with Joint-Embedding Predictive Architectures
FactorGCN
0.366
Factorizable Graph Convolutional Networks
ChebNet
0.360
An Experimental Study of the Transferability of Spectral Graph Networks
BoP
0.297
From Primes to Paths: Enabling Fast Multi-Relational Graph Analysis
MMA
0.156
Multi-Mask Aggregators for Graph Neural Networks
PNA
0.142
Principal Neighbourhood Aggregation for Graph Nets
CRaWl
0.101
Walking Out of the Weisfeiler Leman Hierarchy: Graph Learning Beyond Message Passing
PIN
0.096
Weisfeiler and Lehman Go Paths: Learning Topological Features via Path Complexes
CIN-small
0.094
Weisfeiler and Lehman Go Cellular: CW Networks
CIN++-small
0.091
CIN++: Enhancing Topological Message Passing
CRaWl+VN
0.088
Walking Out of the Weisfeiler Leman Hierarchy: Graph Learning Beyond Message Passing
CIN
0.079
Weisfeiler and Lehman Go Cellular: CW Networks
EIGENFORMER
0.077
Graph Transformers without Positional Encodings
CIN++-500k
0.077
CIN++: Enhancing Topological Message Passing
GraphMLPMixer
0.075 ± 0.001
A Generalization of ViT/MLP-Mixer to Graphs
CIN++
0.074
CIN++: Enhancing Topological Message Passing
SAGNN
0.072±0.002
Substructure Aware Graph Neural Networks
GPS
0.070 ± 0.002
Recipe for a General, Powerful, Scalable Graph Transformer
GINE
0.070 ± 0.004
Recipe for a General, Powerful, Scalable Graph Transformer
PDF
0.066 ± 0.002
Towards Better Graph Representation Learning with Parameterized Decomposition & Filtering
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