The regression task is similar to graph classification but using different loss function and performance metric.
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Graph Neural Networks (GNNs) have been shown to be effective models for different predictive tasks on graph-structured data.
Ranked #2 on Graph Classification on CIFAR10 100k
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.
Ranked #1 on Graph Classification on IPC-grounded
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.
Ranked #2 on Node Classification on Flickr
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.
Ranked #2 on Node Classification on Wiki-Vote
In this paper we present Spektral, an open-source Python library for building graph neural networks with TensorFlow and the Keras application programming interface.
Supervised learning on molecules has incredible potential to be useful in chemistry, drug discovery, and materials science.
Ranked #4 on Graph Regression on ZINC-500k
We show that the expressive models significantly outperform simple scalable baselines, indicating an opportunity for dedicated efforts to further improve graph ML at scale.
Ranked #1 on Node Classification on MAG240M-LSC
Here, we present a theoretical framework for analyzing the expressive power of GNNs to capture different graph structures.
Ranked #1 on Graph Classification on RE-M5K
Graph Convolutional Networks (GCNs) and their variants have experienced significant attention and have become the de facto methods for learning graph representations.
Ranked #2 on Graph Regression on Lipophilicity