# Graph Learning

185 papers with code • 1 benchmarks • 3 datasets

## Libraries

Use these libraries to find Graph Learning models and implementations## Most implemented papers

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

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

# Graph Random Neural Network for Semi-Supervised Learning on Graphs

We study the problem of semi-supervised learning on graphs, for which graph neural networks (GNNs) have been extensively explored.

# GCC: Graph Contrastive Coding for Graph Neural Network Pre-Training

Graph representation learning has emerged as a powerful technique for addressing real-world problems.

# A Unified Framework for Structured Graph Learning via Spectral Constraints

Then we develop an optimization framework that leverages graph learning with specific structures via spectral constraints on graph matrices.

# Graph Construction from Data using Non Negative Kernel regression (NNK Graphs)

Data driven graph constructions are often used in various applications, including several machine learning tasks, where the goal is to make predictions and discover patterns.

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

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

# DeeperGCN: All You Need to Train Deeper GCNs

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

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

Enabling effective and efficient machine learning (ML) over large-scale graph data (e. g., graphs with billions of edges) can have a great impact on both industrial and scientific applications.

# Bilinear Scoring Function Search for Knowledge Graph Learning

We first set up a search space for AutoBLM by analyzing existing scoring functions.

# Learning Laplacian Matrix in Smooth Graph Signal Representations

We show that the Gaussian prior leads to an efficient representation that favors the smoothness property of the graph signals.