Graph Embedding

214 papers with code • 1 benchmarks • 7 datasets

Graph embeddings learn a mapping from a network to a vector space, while preserving relevant network properties.

( Image credit: GAT )

Greatest papers with code

Is a Single Embedding Enough? Learning Node Representations that Capture Multiple Social Contexts

google-research/google-research 6 May 2019

Recent interest in graph embedding methods has focused on learning a single representation for each node in the graph.

Graph Embedding Link Prediction

Graph Attention Networks

labmlai/annotated_deep_learning_paper_implementations ICLR 2018

We present graph attention networks (GATs), novel neural network architectures that operate on graph-structured data, leveraging masked self-attentional layers to address the shortcomings of prior methods based on graph convolutions or their approximations.

Document Classification Graph Attention +7

PyTorch-BigGraph: A Large-scale Graph Embedding System

facebookresearch/PyTorch-BigGraph 28 Mar 2019

Graph embedding methods produce unsupervised node features from graphs that can then be used for a variety of machine learning tasks.

 Ranked #1 on Link Prediction on YouTube (Macro F1 metric)

Graph Embedding graph partitioning +1

struc2vec: Learning Node Representations from Structural Identity

shenweichen/GraphEmbedding 11 Apr 2017

Implementation and experiments of graph embedding algorithms. deep walk, LINE(Large-scale Information Network Embedding), node2vec, SDNE(Structural Deep Network Embedding), struc2vec

Graph Embedding Network Embedding +1

LINE: Large-scale Information Network Embedding

shenweichen/GraphEmbedding 12 Mar 2015

This paper studies the problem of embedding very large information networks into low-dimensional vector spaces, which is useful in many tasks such as visualization, node classification, and link prediction.

Graph Embedding Link Prediction +2

Learning Embeddings from Knowledge Graphs With Numeric Edge Attributes

Accenture/AmpliGraph 18 May 2021

Numeric values associated to edges of a knowledge graph have been used to represent uncertainty, edge importance, and even out-of-band knowledge in a growing number of scenarios, ranging from genetic data to social networks.

Knowledge Graph Embedding Knowledge Graphs

Poincaré Embeddings for Learning Hierarchical Representations

facebookresearch/poincare-embeddings NeurIPS 2017

Representation learning has become an invaluable approach for learning from symbolic data such as text and graphs.

Graph Embedding Hierarchical structure

Asymmetric Transitivity Preserving Graph Embedding

benedekrozemberczki/karateclub ‏‏‎ ‎ 2020

In particular, we develop a novel graph embedding algorithm, High-Order Proximity preserved Embedding (HOPE for short), which is scalable to preserve high-order proximities of large scale graphs and capable of capturing the asymmetric transitivity.

Graph Embedding Link Prediction

Karate Club: An API Oriented Open-source Python Framework for Unsupervised Learning on Graphs

benedekrozemberczki/karateclub CIKM 2020

We present Karate Club a Python framework combining more than 30 state-of-the-art graph mining algorithms which can solve unsupervised machine learning tasks.

Community Detection Graph Classification +3