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

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Datasets

Greatest papers with code

Principal Neighbourhood Aggregation for Graph Nets

NeurIPS 2020 rusty1s/pytorch_geometric

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

GRAPH CLASSIFICATION GRAPH REGRESSION NODE CLASSIFICATION

Semi-Supervised Classification with Graph Convolutional Networks

9 Sep 2016tkipf/gcn

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.

CLASSIFICATION DOCUMENT CLASSIFICATION GRAPH CLASSIFICATION GRAPH REGRESSION NODE CLASSIFICATION SKELETON BASED ACTION RECOGNITION

Inductive Representation Learning on Large Graphs

NeurIPS 2017 williamleif/GraphSAGE

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 LINK PREDICTION NODE CLASSIFICATION REPRESENTATION LEARNING

Graph Attention Networks

ICLR 2018 PetarV-/GAT

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 CLASSIFICATION GRAPH EMBEDDING GRAPH REGRESSION LINK PREDICTION NODE CLASSIFICATION SKELETON BASED ACTION RECOGNITION

Graph Neural Networks in TensorFlow and Keras with Spektral

22 Jun 2020danielegrattarola/spektral

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

CLASSIFICATION GRAPH CLASSIFICATION GRAPH REGRESSION NODE CLASSIFICATION

Benchmarking Graph Neural Networks

2 Mar 2020graphdeeplearning/benchmarking-gnns

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

GRAPH CLASSIFICATION GRAPH REGRESSION LINK PREDICTION NODE CLASSIFICATION

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

17 Mar 2021snap-stanford/ogb

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 KNOWLEDGE GRAPHS LINK PREDICTION NODE CLASSIFICATION

How Powerful are Graph Neural Networks?

ICLR 2019 weihua916/powerful-gnns

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

CLASSIFICATION GRAPH CLASSIFICATION GRAPH REGRESSION GRAPH REPRESENTATION LEARNING NODE CLASSIFICATION

Simplifying Graph Convolutional Networks

19 Feb 2019Tiiiger/SGC

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 NODE CLASSIFICATION RELATION EXTRACTION SENTIMENT ANALYSIS SKELETON BASED ACTION RECOGNITION TEXT CLASSIFICATION