Graph Representation Learning

116 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

Greatest papers with code

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.

Graph Classification Graph Representation Learning +2

WikiGraphs: A Wikipedia Text - Knowledge Graph Paired Dataset

deepmind/deepmind-research 20 Jul 2021

We present a new dataset of Wikipedia articles each paired with a knowledge graph, to facilitate the research in conditional text generation, graph generation and graph representation learning.

Conditional Text Generation Graph Generation +2

Large-scale graph representation learning with very deep GNNs and self-supervision

deepmind/deepmind-research 20 Jul 2021

In doing so, we demonstrate evidence of scalable self-supervised graph representation learning, and utility of very deep GNNs -- both very important open issues.

Denoising Graph Representation Learning

A Survey on Knowledge Graphs: Representation, Acquisition and Applications

shaoxiongji/awesome-knowledge-graph 2 Feb 2020

In this survey, we provide a comprehensive review of knowledge graph covering overall research topics about 1) knowledge graph representation learning, 2) knowledge acquisition and completion, 3) temporal knowledge graph, and 4) knowledge-aware applications, and summarize recent breakthroughs and perspective directions to facilitate future research.

Knowledge Graph Completion Knowledge Graph Embedding +1

Graph Neural Networks for Natural Language Processing: A Survey

graph4ai/graph4nlp 10 Jun 2021

Deep learning has become the dominant approach in coping with various tasks in Natural LanguageProcessing (NLP).

graph construction Graph Representation Learning

EvolveGCN: Evolving Graph Convolutional Networks for Dynamic Graphs

benedekrozemberczki/pytorch_geometric_temporal 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.

General Classification Graph Representation Learning +2

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.

General Classification Graph Classification +3

CogDL: Toolkit for Deep Learning on Graphs

THUDM/cogdl 1 Mar 2021

It provides standard training and evaluation for the most important tasks in the graph domain, including node classification, graph classification, etc.

Graph Classification Graph Embedding +5

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.

Graph Representation Learning Link Prediction +1