Graph Regression
99 papers with code • 12 benchmarks • 18 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
SupplyGraph
GlassTemp
Most implemented papers
Graph Attention Networks
PetarV-/GAT
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ICLR 2018
We present graph attention networks (GATs), novel neural network architectures that operate on graph-structured data, leveraging masked self-attentional layers to address the shortcomings of prior methods based on graph convolutions or their approximations.
Semi-Supervised Classification with Graph Convolutional Networks
tkipf/pygcn
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We present a scalable approach for semi-supervised learning on graph-structured data that is based on an efficient variant of convolutional neural networks which operate directly on graphs.
Neural Message Passing for Quantum Chemistry
brain-research/mpnn
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ICML 2017
Supervised learning on molecules has incredible potential to be useful in chemistry, drug discovery, and materials science.
Inductive Representation Learning on Large Graphs
williamleif/GraphSAGE
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NeurIPS 2017
Low-dimensional embeddings of nodes in large graphs have proved extremely useful in a variety of prediction tasks, from content recommendation to identifying protein functions.
How Powerful are Graph Neural Networks?
weihua916/powerful-gnns
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ICLR 2019
Here, we present a theoretical framework for analyzing the expressive power of GNNs to capture different graph structures.
Benchmarking Graph Neural Networks
graphdeeplearning/benchmarking-gnns
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In the last few years, graph neural networks (GNNs) have become the standard toolkit for analyzing and learning from data on graphs.
Convolutional Networks on Graphs for Learning Molecular Fingerprints
HIPS/neural-fingerprint
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NeurIPS 2015
We introduce a convolutional neural network that operates directly on graphs.
Principal Neighbourhood Aggregation for Graph Nets
lukecavabarrett/pna
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NeurIPS 2020
Graph Neural Networks (GNNs) have been shown to be effective models for different predictive tasks on graph-structured data.
Simplifying Graph Convolutional Networks
Tiiiger/SGC
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Graph Convolutional Networks (GCNs) and their variants have experienced significant attention and have become the de facto methods for learning graph representations.
OGB-LSC: A Large-Scale Challenge for Machine Learning on Graphs
Enabling effective and efficient machine learning (ML) over large-scale graph data (e. g., graphs with billions of edges) can have a great impact on both industrial and scientific applications.
