# Graph Representation Learning

284 papers with code • 1 benchmarks • 4 datasets

The goal of **Graph Representation Learning** is to construct a set of features (‘embeddings’) representing the structure of the graph and the data thereon. We can distinguish among Node-wise embeddings, representing each node of the graph, Edge-wise embeddings, representing each edge in the graph, and Graph-wise embeddings representing the graph as a whole.

## Libraries

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

# How Powerful are Graph Neural Networks?

Here, we present a theoretical framework for analyzing the expressive power of GNNs to capture different graph structures.

# Hierarchical Graph Representation Learning with Differentiable Pooling

Recently, graph neural networks (GNNs) have revolutionized the field of graph representation learning through effectively learned node embeddings, and achieved state-of-the-art results in tasks such as node classification and link prediction.

# EvolveGCN: Evolving Graph Convolutional Networks for Dynamic Graphs

Existing approaches typically resort to node embeddings and use a recurrent neural network (RNN, broadly speaking) to regulate the embeddings and learn the temporal dynamics.

# GraphSAINT: Graph Sampling Based Inductive Learning Method

Graph Convolutional Networks (GCNs) are powerful models for learning representations of attributed graphs.

# GraphGAN: Graph Representation Learning with Generative Adversarial Nets

The goal of graph representation learning is to embed each vertex in a graph into a low-dimensional vector space.

# Fast Graph Representation Learning with PyTorch Geometric

We introduce PyTorch Geometric, a library for deep learning on irregularly structured input data such as graphs, point clouds and manifolds, built upon PyTorch.

# A Fair Comparison of Graph Neural Networks for Graph Classification

We believe that this work can contribute to the development of the graph learning field, by providing a much needed grounding for rigorous evaluations of graph classification models.

# Understanding Negative Sampling in Graph Representation Learning

To the best of our knowledge, we are the first to derive the theory and quantify that the negative sampling distribution should be positively but sub-linearly correlated to their positive sampling distribution.

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

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

# QA-GNN: Reasoning with Language Models and Knowledge Graphs for Question Answering

The problem of answering questions using knowledge from pre-trained language models (LMs) and knowledge graphs (KGs) presents two challenges: given a QA context (question and answer choice), methods need to (i) identify relevant knowledge from large KGs, and (ii) perform joint reasoning over the QA context and KG.