# Graph Regression

51 papers with code • 11 benchmarks • 13 datasets

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

## Libraries

Use these libraries to find Graph Regression models and implementations## Datasets

## Most implemented papers

# Graph Attention Networks

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

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

Supervised learning on molecules has incredible potential to be useful in chemistry, drug discovery, and materials science.

# How Powerful are Graph Neural Networks?

Here, we present a theoretical framework for analyzing the expressive power of GNNs to capture different graph structures.

# Inductive Representation Learning on Large Graphs

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.

# Benchmarking Graph Neural Networks

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

We introduce a convolutional neural network that operates directly on graphs.

# Simplifying Graph Convolutional Networks

Graph Convolutional Networks (GCNs) and their variants have experienced significant attention and have become the de facto methods for learning graph representations.

# Principal Neighbourhood Aggregation for Graph Nets

Graph Neural Networks (GNNs) have been shown to be effective models for different predictive tasks on graph-structured data.

# 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.