Graph Embedding
214 papers with code • 1 benchmarks • 7 datasets
Graph embeddings learn a mapping from a network to a vector space, while preserving relevant network properties.
( Image credit: GAT )
Benchmarks
| Trend | Dataset | Best Model | Paper Title | Paper | Code | Compare |
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OLPBENCH
Subtasks
Greatest papers with code
Is a Single Embedding Enough? Learning Node Representations that Capture Multiple Social Contexts
google-research/google-research
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•
Recent interest in graph embedding methods has focused on learning a single representation for each node in the graph.
Graph Attention Networks
labmlai/annotated_deep_learning_paper_implementations
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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.
Ranked #2 on
Node Classification
on Wiki-Vote
PyTorch-BigGraph: A Large-scale Graph Embedding System
facebookresearch/PyTorch-BigGraph
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Graph embedding methods produce unsupervised node features from graphs that can then be used for a variety of machine learning tasks.
Ranked #1 on
Link Prediction
on YouTube
(Macro F1 metric)
struc2vec: Learning Node Representations from Structural Identity
shenweichen/GraphEmbedding
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Implementation and experiments of graph embedding algorithms. deep walk, LINE(Large-scale Information Network Embedding), node2vec, SDNE(Structural Deep Network Embedding), struc2vec
Ranked #2 on
Node Classification
on Wikipedia
LINE: Large-scale Information Network Embedding
shenweichen/GraphEmbedding
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•
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.
Ranked #5 on
Node Classification
on Wikipedia
Learning Embeddings from Knowledge Graphs With Numeric Edge Attributes
Numeric values associated to edges of a knowledge graph have been used to represent uncertainty, edge importance, and even out-of-band knowledge in a growing number of scenarios, ranging from genetic data to social networks.
Probability Calibration for Knowledge Graph Embedding Models
Accenture/AmpliGraph
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ICLR 2020
We show popular embedding models are indeed uncalibrated.
Calibration for Link Prediction
Knowledge Graph Embedding
+1
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.
Ranked #1 on
Lexical Entailment
on HyperLex
Asymmetric Transitivity Preserving Graph Embedding
In particular, we develop a novel graph embedding algorithm, High-Order Proximity preserved Embedding (HOPE for short), which is scalable to preserve high-order proximities of large scale graphs and capable of capturing the asymmetric transitivity.
Karate Club: An API Oriented Open-source Python Framework for Unsupervised Learning on Graphs
We present Karate Club a Python framework combining more than 30 state-of-the-art graph mining algorithms which can solve unsupervised machine learning tasks.
