Graph Learning

103 papers with code • 0 benchmarks • 3 datasets

This task has no description! Would you like to contribute one?

Greatest papers with code

Diffusion Improves Graph Learning

rusty1s/pytorch_geometric NeurIPS 2019

In this work, we remove the restriction of using only the direct neighbors by introducing a powerful, yet spatially localized graph convolution: Graph diffusion convolution (GDC).

Clustering Graph Learning +1

Deep Graph Library: A Graph-Centric, Highly-Performant Package for Graph Neural Networks

dmlc/dgl 3 Sep 2019

Advancing research in the emerging field of deep graph learning requires new tools to support tensor computation over graphs.

Graph Learning Node Classification

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

Graph Agreement Models for Semi-Supervised Learning

tensorflow/neural-structured-learning NeurIPS 2019

To address this, we propose Graph Agreement Models (GAM), which introduces an auxiliary model that predicts the probability of two nodes sharing the same label as a learned function of their features.

Classification General Classification +2

Graph-RISE: Graph-Regularized Image Semantic Embedding

tensorflow/neural-structured-learning 14 Feb 2019

Learning image representations to capture fine-grained semantics has been a challenging and important task enabling many applications such as image search and clustering.

Clustering General Classification +3

Connecting the Dots: Multivariate Time Series Forecasting with Graph Neural Networks

benedekrozemberczki/pytorch_geometric_temporal 24 May 2020

Modeling multivariate time series has long been a subject that has attracted researchers from a diverse range of fields including economics, finance, and traffic.

Graph Learning Multivariate Time Series Forecasting +1

DeeperGCN: All You Need to Train Deeper GCNs

lightaime/deep_gcns_torch 13 Jun 2020

Graph Convolutional Networks (GCNs) have been drawing significant attention with the power of representation learning on graphs.

Graph Learning Representation Learning

AutoGL: A Library for Automated Graph Learning

THUMNLab/AutoGL 11 Apr 2021

To fill this gap, we present Automated Graph Learning (AutoGL), the first library for automated machine learning on graphs.

AutoML Feature Engineering +1

Automated Machine Learning on Graphs: A Survey

THUMNLab/AutoGL 1 Mar 2021

Machine learning on graphs has been extensively studied in both academic and industry.

Graph Learning Neural Architecture Search