Graph Embedding

264 papers with code • 1 benchmarks • 8 datasets

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

( Image credit: GAT )


Use these libraries to find Graph Embedding models and implementations

Most implemented papers

Graph Attention Networks

PetarV-/GAT 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.

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.

LINE: Large-scale Information Network Embedding

tangjianpku/LINE 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.

Learning Combinatorial Optimization Algorithms over Graphs

Hanjun-Dai/graph_comb_opt NeurIPS 2017

The design of good heuristics or approximation algorithms for NP-hard combinatorial optimization problems often requires significant specialized knowledge and trial-and-error.

struc2vec: Learning Node Representations from Structural Identity

leoribeiro/struc2vec 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

AutoSF: Searching Scoring Functions for Knowledge Graph Embedding

AutoML-Research/AutoSF 26 Apr 2019

The algorithm is further sped up by a filter and a predictor, which can avoid repeatedly training SFs with same expressive ability and help removing bad candidates during the search before model training.

GraphSAINT: Graph Sampling Based Inductive Learning Method


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

Learning Hierarchy-Aware Knowledge Graph Embeddings for Link Prediction


HAKE is inspired by the fact that concentric circles in the polar coordinate system can naturally reflect the hierarchy.

Graph Embedding Techniques, Applications, and Performance: A Survey

palash1992/GEM 8 May 2017

Graphs, such as social networks, word co-occurrence networks, and communication networks, occur naturally in various real-world applications.

graph2vec: Learning Distributed Representations of Graphs

benedekrozemberczki/karateclub 17 Jul 2017

Recent works on representation learning for graph structured data predominantly focus on learning distributed representations of graph substructures such as nodes and subgraphs.