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Graph Regression

9 papers with code · Graphs

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

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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.

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

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 EMBEDDING GRAPH REGRESSION LINK PREDICTION NODE CLASSIFICATION SKELETON BASED ACTION RECOGNITION

Neural Message Passing for Quantum Chemistry

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

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

DRUG DISCOVERY FORMATION ENERGY GRAPH REGRESSION 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 RELATION EXTRACTION SENTIMENT ANALYSIS SKELETON BASED ACTION RECOGNITION TEXT CLASSIFICATION

Residual Gated Graph ConvNets

ICLR 2018 xbresson/spatial_graph_convnets

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.

GRAPH CLASSIFICATION GRAPH CLUSTERING GRAPH REGRESSION REGRESSION

Molecular Property Prediction: A Multilevel Quantum Interactions Modeling Perspective

25 Jun 2019tencent-alchemy/Alchemy

In this paper, we propose a generalizable and transferable Multilevel Graph Convolutional neural Network (MGCN) for molecular property prediction.

GRAPH REGRESSION

Deriving Neural Architectures from Sequence and Graph Kernels

ICML 2017 taolei87/icml17_knn

The design of neural architectures for structured objects is typically guided by experimental insights rather than a formal process.

GRAPH REGRESSION LANGUAGE MODELLING REGRESSION

Molecule Property Prediction Based on Spatial Graph Embedding

Journal of Chemical Information and Modeling 2019 wxfsd/C-SGEN

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 )

DRUG DISCOVERY GRAPH EMBEDDING GRAPH REGRESSION