Graph Representation Learning

210 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.

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.

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.

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

michiyasunaga/qagnn NAACL 2021

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.

Do Transformers Really Perform Bad for Graph Representation?

Microsoft/Graphormer 9 Jun 2021

Our key insight to utilizing Transformer in the graph is the necessity of effectively encoding the structural information of a graph into the model.

Hyperbolic Neural Networks

dalab/hyperbolic_nn NeurIPS 2018

However, the representational power of hyperbolic geometry is not yet on par with Euclidean geometry, mostly because of the absence of corresponding hyperbolic neural network layers.