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
Latest papers with no code
Advancing Graph Neural Networks with HL-HGAT: A Hodge-Laplacian and Attention Mechanism Approach for Heterogeneous Graph-Structured Data
Additionally, we propose a pooling operator to coarsen $k$-simplices, combining features through simplicial attention mechanisms of self-attention and cross-attention via transformers and SP operators, capturing topological interconnections across multiple dimensions of simplices.
Gaussian process regression with Sliced Wasserstein Weisfeiler-Lehman graph kernels
Supervised learning has recently garnered significant attention in the field of computational physics due to its ability to effectively extract complex patterns for tasks like solving partial differential equations, or predicting material properties.
Topology-Informed Graph Transformer
Transformers have revolutionized performance in Natural Language Processing and Vision, paving the way for their integration with Graph Neural Networks (GNNs).
Out-of-Distribution Generalized Dynamic Graph Neural Network for Human Albumin Prediction
We first model human albumin prediction as a dynamic graph regression problem to model the dynamics and patient relationship.
Advancing Fluid-Based Thermal Management Systems Design: Leveraging Graph Neural Networks for Graph Regression and Efficient Enumeration Reduction
The dynamics of these system architectures are modeled under various loading conditions, and an open-loop optimal controller is employed to determine each system's optimal performance.
Graph Neural Networks with a Distribution of Parametrized Graphs
Traditionally, graph neural networks have been trained using a single observed graph.
Equivariant Matrix Function Neural Networks
Graph Neural Networks (GNNs), especially message-passing neural networks (MPNNs), have emerged as powerful architectures for learning on graphs in diverse applications.
Weisfeiler and Lehman Go Paths: Learning Topological Features via Path Complexes
Graph Neural Networks (GNNs), despite achieving remarkable performance across different tasks, are theoretically bounded by the 1-Weisfeiler-Lehman test, resulting in limitations in terms of graph expressivity.
Neural Priority Queues for Graph Neural Networks
Graph Neural Networks (GNNs) have shown considerable success in neural algorithmic reasoning.
RegExplainer: Generating Explanations for Graph Neural Networks in Regression Task
Graph regression is a fundamental task and has received increasing attention in a wide range of graph learning tasks.
