The regression task is similar to graph classification but using different loss function and performance metric.
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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.
SOTA for Graph Classification on IPC-lifted
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.
#2 best model for Skeleton Based Action Recognition on J-HMBD Early Action
Supervised learning on molecules has incredible potential to be useful in chemistry, drug discovery, and materials science.
SOTA for Drug Discovery on QM9
Graph Convolutional Networks (GCNs) and their variants have experienced significant attention and have become the de facto methods for learning graph representations.
SOTA for Text Classification on Ohsumed
In this paper, we are interested to design neural networks for graphs with variable length in order to solve learning problems such as vertex classification, graph classification, graph regression, and graph generative tasks.
In this paper, we propose a generalizable and transferable Multilevel Graph Convolutional neural Network (MGCN) for molecular property prediction.
#5 best model for Graph Regression on Lipophilicity
And, multiple C-SGELs are stacked to construct a convolution spatial graph embedding network (C-SGEN) for end-to-end representation learning.
SOTA for Graph Regression on Lipophilicity (RMSE metric )