Graph Regression

35 papers with code • 8 benchmarks • 6 datasets

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

Greatest papers with code

Principal Neighbourhood Aggregation for Graph Nets

rusty1s/pytorch_geometric NeurIPS 2020

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

Graph Classification Graph Regression +1

Semi-Supervised Classification with Graph Convolutional Networks

tkipf/gcn 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.

Document Classification General Classification +4

Graph Attention Networks

labmlai/annotated_deep_learning_paper_implementations 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.

Document Classification Graph Attention +7

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.

Graph Classification Graph Regression +3

Graph Neural Networks in TensorFlow and Keras with Spektral

danielegrattarola/spektral 22 Jun 2020

In this paper we present Spektral, an open-source Python library for building graph neural networks with TensorFlow and the Keras application programming interface.

General Classification Graph Classification +2

Benchmarking Graph Neural Networks

graphdeeplearning/benchmarking-gnns 2 Mar 2020

Graph neural networks (GNNs) have become the standard toolkit for analyzing and learning from data on graphs.

Graph Classification Graph Regression +2

OGB-LSC: A Large-Scale Challenge for Machine Learning on Graphs

snap-stanford/ogb 17 Mar 2021

We show that the expressive models significantly outperform simple scalable baselines, indicating an opportunity for dedicated efforts to further improve graph ML at scale.

Graph Learning Graph Regression +3

Neural Message Passing for Quantum Chemistry

Microsoft/gated-graph-neural-network-samples ICML 2017

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

Drug Discovery Formation Energy +3

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.

General Classification Graph Classification +3

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.

Graph Regression Image Classification +5