SOTAVerified

Graph Regression

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

Papers

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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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All datasetsZINC-500kZINCPCQM4Mv2-LSCLipophilicityPCQM4M-LSCPeptides-structZINC 100kTox21ZINC-fullESOLQM9QM9: del e

Benchmark Results

#ModelMetricClaimedVerifiedStatus
1GATMAE0.46—Unverified
2MoNetMAE0.41—Unverified
3GatedGCNMAE0.36—Unverified
4MPNNMAE0.29—Unverified
5EGTMAE0.14—Unverified
6ARGNPMAE0.14—Unverified
7GSNMAE0.12—Unverified
8CIN-smallMAE0.09—Unverified