SOTAVerified

Graph Regression

The regression task is similar to graph classification but using different loss function and performance metric.

Papers

Showing 1–10 of 145 papers

TitleStatusHype
Graph Neural Networks for Jamming Source LocalizationCode0
A Benchmark Dataset for Graph Regression with Homogeneous and Multi-Relational Variants—0
Improving the Effective Receptive Field of Message-Passing Neural NetworksCode1
GotenNet: Rethinking Efficient 3D Equivariant Graph Neural NetworksCode2
Power Spectrum Signatures of Graphs—0
Pre-training Graph Neural Networks on Molecules by Using Subgraph-Conditioned Graph Information BottleneckCode1
Unlocking the Potential of Classic GNNs for Graph-level Tasks: Simple Architectures Meet ExcellenceCode2
Learning Efficient Positional Encodings with Graph Neural NetworksCode1
Beyond Message Passing: Neural Graph Pattern MachineCode1
Molecular Fingerprints Are Strong Models for Peptide Function PredictionCode3
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1CKGCNMAE5.9—Unverified
2GINMAE0.53—Unverified
3GraphSageMAE0.4—Unverified
4RingGNNMAE0.35—Unverified
53WLGNNMAE0.3—Unverified
6MoNetMAE0.29—Unverified
7GatedGCNMAE0.28—Unverified
8MPNN (max)MAE0.25—Unverified
9GatedGCN-E-PEMAE0.21—Unverified
10GatedGCN-PEMAE0.21—Unverified