Graph Embedding
264 papers with code • 1 benchmarks • 8 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
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Libraries
Use these libraries to find Graph Embedding models and implementations
BioGRID
LastFM Asia
OLPBENCH
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.
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.
struc2vec: Learning Node Representations from Structural Identity
Implementation and experiments of graph embedding algorithms. deep walk, LINE(Large-scale Information Network Embedding), node2vec, SDNE(Structural Deep Network Embedding), struc2vec
AutoSF: Searching Scoring Functions for Knowledge Graph Embedding
The algorithm is further sped up by a filter and a predictor, which can avoid repeatedly training SFs with same expressive ability and help removing bad candidates during the search before model training.
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.
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.
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.
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.
