Graph Embedding
Graph embeddings learn a mapping from a network to a vector space, while preserving relevant network properties.
State-of-the-art leaderboards
Subtasks
Greatest papers with code
PyTorch-BigGraph: A Large-scale Graph Embedding System
• facebookresearch/PyTorch-BigGraph
• 
Graph embedding methods produce unsupervised node features from graphs that can then be used for a variety of machine learning tasks.
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 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.
#3 best model for
Document Classification
on Cora
DOCUMENT CLASSIFICATION GRAPH EMBEDDING LINK PREDICTION NODE CLASSIFICATION
Graph Embedding Techniques, Applications, and Performance: A Survey
Graphs, such as social networks, word co-occurrence networks, and communication networks, occur naturally in various real-world applications.
LINE: Large-scale Information Network Embedding
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.
#5 best model for
Node Classification
on Wikipedia
GRAPH EMBEDDING LINK PREDICTION NETWORK EMBEDDING NODE CLASSIFICATION
graph2vec: Learning Distributed Representations of Graphs
• benedekrozemberczki/graph2vec
•
Recent works on representation learning for graph structured data predominantly focus on learning distributed representations of graph substructures such as nodes and subgraphs.
SOTA for Graph Classification on NCI109
struc2vec: Learning Node Representations from Structural Identity
• shenweichen/GraphEmbedding
•
Implementation and experiments of graph embedding algorithms. deep walk, LINE(Large-scale Information Network Embedding), node2vec, SDNE(Structural Deep Network Embedding), struc2vec
#2 best model for
Node Classification
on Wikipedia
DeepPath: A Reinforcement Learning Method for Knowledge Graph Reasoning
We study the problem of learning to reason in large scale knowledge graphs (KGs).
KNOWLEDGE GRAPH EMBEDDING KNOWLEDGE GRAPH EMBEDDINGS KNOWLEDGE GRAPHS
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.
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.