Graph Embedding
Graph embeddings learn a mapping from a network to a vector space, while preserving relevant network properties.
( Image credit: GAT )
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.
#2 best model for
Skeleton Based Action Recognition
on J-HMBD Early Action
DOCUMENT CLASSIFICATION GRAPH EMBEDDING GRAPH REGRESSION LINK PREDICTION NODE CLASSIFICATION SKELETON BASED ACTION RECOGNITION
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.
#9 best model for
Graph Classification
on NCI109
GraphVAE: Towards Generation of Small Graphs Using Variational Autoencoders
ICLR 2018
• JiaxuanYou/graph-generation
•
Deep learning on graphs has become a popular research topic with many applications.
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 Embedding Networks for Deep Graph Matching
ICCV 2019
• Thinklab-SJTU/PCA-GM
•
In addition with its NP-completeness nature, another important challenge is effective modeling of the node-wise and structure-wise affinity across graphs and the resulting objective, to guide the matching procedure effectively finding the true matching against noises.