Graph Embedding
Graph embeddings learn a mapping from a network to a vector space, while preserving relevant network properties.
State-of-the-art leaderboards
| Trend | Dataset | Best Method | Paper title | Paper | Code | Compare |
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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
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
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
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
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.
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.