Browse > Graphs > Representation Learning > Graph Embedding

Graph Embedding

47 papers with code · Graphs

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

State-of-the-art leaderboards

No evaluation results yet. Help compare methods by submit evaluation metrics.

Greatest papers with code

PyTorch-BigGraph: A Large-scale Graph Embedding System

28 Mar 2019facebookresearch/PyTorch-BigGraph

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

GRAPH EMBEDDING LINK PREDICTION

Poincaré Embeddings for Learning Hierarchical Representations

NeurIPS 2017 facebookresearch/poincare-embeddings

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

GRAPH EMBEDDING

Graph Attention Networks

ICLR 2018 PetarV-/GAT

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 EMBEDDING LINK PREDICTION NODE CLASSIFICATION

Graph Embedding Techniques, Applications, and Performance: A Survey

8 May 2017palash1992/GEM

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

GRAPH EMBEDDING

LINE: Large-scale Information Network Embedding

12 Mar 2015tangjianpku/LINE

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 NETWORK EMBEDDING NODE CLASSIFICATION

graph2vec: Learning Distributed Representations of Graphs

17 Jul 2017benedekrozemberczki/graph2vec

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

GRAPH CLASSIFICATION GRAPH EMBEDDING GRAPH MATCHING

struc2vec: Learning Node Representations from Structural Identity

11 Apr 2017shenweichen/GraphEmbedding

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 NODE CLASSIFICATION

Learning Combinatorial Optimization Algorithms over Graphs

NeurIPS 2017 Hanjun-Dai/graph_comb_opt

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

COMBINATORIAL OPTIMIZATION GRAPH EMBEDDING

GraphVAE: Towards Generation of Small Graphs Using Variational Autoencoders

ICLR 2018 snap-stanford/GraphRNN

Deep learning on graphs has become a popular research topic with many applications.

GRAPH EMBEDDING