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
3 papers
96
2 papers
1,092
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Most implemented papers

Graph Attention Networks

PetarV-/GAT 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

dmlc/dgl 9 Sep 2016

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 ICML 2017

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

How Powerful are Graph Neural Networks?

weihua916/powerful-gnns ICLR 2019

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

Inductive Representation Learning on Large Graphs

williamleif/GraphSAGE 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.

Benchmarking Graph Neural Networks

graphdeeplearning/benchmarking-gnns 2 Mar 2020

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 NeurIPS 2015

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

Simplifying Graph Convolutional Networks

Tiiiger/SGC 19 Feb 2019

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

lukecavabarrett/pna NeurIPS 2020

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

dmlc/dgl CVPR 2017

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.