Graph Regression
89 papers with code • 12 benchmarks • 17 datasets
The regression task is similar to graph classification but using different loss function and performance metric.
Benchmarks
These leaderboards are used to track progress in Graph Regression
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Libraries
Use these libraries to find Graph Regression models and implementationsDatasets
ZINC
MoleculeNet
Long Range Graph Benchmark (LRGB)
OGB-LSC
Tox21
PCQM4Mv2-LSC
ESOL (Estimated SOLubility)
$O_2$Perm
GlassTemp
MeltingTemp
Most implemented papers
Triplet Interaction Improves Graph Transformers: Accurate Molecular Graph Learning with Triplet Graph Transformers
We also obtain SOTA results on QM9, MOLPCBA, and LIT-PCBA molecular property prediction benchmarks via transfer learning.
Molecular Graph Convolutions: Moving Beyond Fingerprints
Molecular "fingerprints" encoding structural information are the workhorse of cheminformatics and machine learning in drug discovery applications.
Provably Powerful Graph Networks
hadarser/ProvablyPowerfulGraphNetworks_torch
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NeurIPS 2019
It was shown that the popular message passing GNN cannot distinguish between graphs that are indistinguishable by the 1-WL test (Morris et al. 2018; Xu et al. 2019).
Molecular Property Prediction: A Multilevel Quantum Interactions Modeling Perspective
In this paper, we propose a generalizable and transferable Multilevel Graph Convolutional neural Network (MGCN) for molecular property prediction.
Physics-informed neural networks for corrosion-fatigue prognosis
PML-UCF/pinn
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Annual Conference of the PHM Society 2019
The result is a cumulative damage model where the physics-informed layers are used to model the relatively well understood physics (crack growth through Paris law) and the data-driven layers account for the hard to model effects (bias in damage accumulation due to corrosion).
A physics-informed neural network for wind turbine main bearing fatigue
PML-UCF/pinn
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International Journal of Prognostics and Health Management 2020
Unexpected main bearing failure on a wind turbine causes unwanted maintenance and increased operation costs (mainly due to crane, parts, labor, and production loss).
Optimal Transport Graph Neural Networks
benatorc/OTGNN
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Current graph neural network (GNN) architectures naively average or sum node embeddings into an aggregated graph representation -- potentially losing structural or semantic information.
Improving Graph Neural Network Expressivity via Subgraph Isomorphism Counting
gbouritsas/graph-substructure-networks
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It has been shown that the expressive power of standard GNNs is bounded by the Weisfeiler-Leman (WL) graph isomorphism test, from which they inherit proven limitations such as the inability to detect and count graph substructures.
Directional Graph Networks
Saro00/DGN
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Then, we propose the use of the Laplacian eigenvectors as such vector field.
Transformers Generalize DeepSets and Can be Extended to Graphs and Hypergraphs
We present a generalization of Transformers to any-order permutation invariant data (sets, graphs, and hypergraphs).
