Graph Representation Learning

383 papers with code • 1 benchmarks • 6 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.

Source: SIGN: Scalable Inception Graph Neural Networks


Use these libraries to find Graph Representation Learning models and implementations

Most implemented papers

How Powerful are Graph Neural Networks?

weihua916/powerful-gnns ICLR 2019

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

dmlc/dgl NeurIPS 2018

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

IBM/EvolveGCN 26 Feb 2019

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

hwwang55/GraphGAN 22 Nov 2017

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

rusty1s/pytorch_geometric 6 Mar 2019

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

diningphil/gnn-comparison ICLR 2020

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.

SIGN: Scalable Inception Graph Neural Networks

twitter-research/sign 23 Apr 2020

Graph representation learning has recently been applied to a broad spectrum of problems ranging from computer graphics and chemistry to high energy physics and social media.

Understanding Negative Sampling in Graph Representation Learning

zyang-16/MCNS 20 May 2020

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

THUDM/GCC 17 Jun 2020

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