Graph Embedding
449 papers with code • 1 benchmarks • 10 datasets
Graph embeddings learn a mapping from a network to a vector space, while preserving relevant network properties.
( Image credit: GAT )
Benchmarks
These leaderboards are used to track progress in Graph Embedding
| Trend | Dataset | Best Model | Paper | Code | Compare |
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Libraries
Use these libraries to find Graph Embedding models and implementationsDatasets
BioGRID
LastFM Asia
OLPBENCH
HeriGraph
Subtasks
Most implemented papers
Graph Attention Networks
PetarV-/GAT
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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
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NeurIPS 2017
Representation learning has become an invaluable approach for learning from symbolic data such as text and graphs.
RotatE: Knowledge Graph Embedding by Relational Rotation in Complex Space
DeepGraphLearning/KnowledgeGraphEmbedding
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ICLR 2019
We study the problem of learning representations of entities and relations in knowledge graphs for predicting missing links.
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.
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.
Learning Combinatorial Optimization Algorithms over Graphs
The design of good heuristics or approximation algorithms for NP-hard combinatorial optimization problems often requires significant specialized knowledge and trial-and-error.
Inductive Relation Prediction by Subgraph Reasoning
The dominant paradigm for relation prediction in knowledge graphs involves learning and operating on latent representations (i. e., embeddings) of entities and relations.
GraphSAINT: Graph Sampling Based Inductive Learning Method
GraphSAINT/GraphSAINT
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ICLR 2020
Graph Convolutional Networks (GCNs) are powerful models for learning representations of attributed graphs.
graph2vec: Learning Distributed Representations of Graphs
Recent works on representation learning for graph structured data predominantly focus on learning distributed representations of graph substructures such as nodes and subgraphs.
NSCaching: Simple and Efficient Negative Sampling for Knowledge Graph Embedding
Negative sampling, which samples negative triplets from non-observed ones in the training data, is an important step in KG embedding.
