Graph Regression
88 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
Cylinder in Crossflow
Most implemented papers
Geometric deep learning on graphs and manifolds using mixture model CNNs
Recently, there has been an increasing interest in geometric deep learning, attempting to generalize deep learning methods to non-Euclidean structured data such as graphs and manifolds, with a variety of applications from the domains of network analysis, computational social science, or computer graphics.
Simple and Deep Graph Convolutional Networks
We propose the GCNII, an extension of the vanilla GCN model with two simple yet effective techniques: {\em Initial residual} and {\em Identity mapping}.
Do Transformers Really Perform Bad for Graph Representation?
Our key insight to utilizing Transformer in the graph is the necessity of effectively encoding the structural information of a graph into the model.
A Generalization of Transformer Networks to Graphs
graphdeeplearning/graphtransformer
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This work closes the gap between the original transformer, which was designed for the limited case of line graphs, and graph neural networks, that can work with arbitrary graphs.
Global Self-Attention as a Replacement for Graph Convolution
shamim-hussain/egt_pytorch
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The resultant framework - which we call Edge-augmented Graph Transformer (EGT) - can directly accept, process and output structural information of arbitrary form, which is important for effective learning on graph-structured data.
Benchmarking Graphormer on Large-Scale Molecular Modeling Datasets
This technical note describes the recent updates of Graphormer, including architecture design modifications, and the adaption to 3D molecular dynamics simulation.
Recipe for a General, Powerful, Scalable Graph Transformer
We propose a recipe on how to build a general, powerful, scalable (GPS) graph Transformer with linear complexity and state-of-the-art results on a diverse set of benchmarks.
MEGAN: Multi-Explanation Graph Attention Network
Unlike existing graph explainability methods, our network can produce node and edge attributional explanations along multiple channels, the number of which is independent of task specifications.
A Generalization of ViT/MLP-Mixer to Graphs
XiaoxinHe/Graph-ViT-MLPMixer
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First, they capture long-range dependency and mitigate the issue of over-squashing as demonstrated on Long Range Graph Benchmark and TreeNeighbourMatch datasets.
Laplacian Canonization: A Minimalist Approach to Sign and Basis Invariant Spectral Embedding
pku-ml/laplaciancanonization
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NeurIPS 2023
However, from a theoretical perspective, the universal expressive power of spectral embedding comes at the price of losing two important invariance properties of graphs, sign and basis invariance, which also limits its effectiveness on graph data.
