Graph Embedding Edit Task
Graphs • Representation Learning
About
Graph embeddings learn a mapping from a network to a vector space, while preserving relevant network properties.
( Image credit: GAT )
Benchmarks
| TREND | DATASET | BEST METHOD | PAPER TITLE | PAPER | CODE | COMPARE |
|---|---|---|---|---|---|---|
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Subtasks
Datasets
Greatest papers with code
Is a Single Embedding Enough? Learning Node Representations that Capture Multiple Social Contexts
• google-research/google-research
• 
Recent interest in graph embedding methods has focused on learning a single representation for each node in the graph.
Graph Attention Networks
ICLR 2018
• aymericdamien/TopDeepLearning
•
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
DOCUMENT CLASSIFICATION GRAPH CLASSIFICATION GRAPH EMBEDDING GRAPH REGRESSION LINK PREDICTION NODE CLASSIFICATION SKELETON BASED ACTION RECOGNITION
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.
Ranked #1 on
Link Prediction
on YouTube
(Macro F1 metric)
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
Ranked #2 on
Node Classification
on Wikipedia
LINE: Large-scale Information Network Embedding
• shenweichen/GraphEmbedding
•
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
GRAPH EMBEDDING LINK PREDICTION NETWORK EMBEDDING NODE CLASSIFICATION
Probability Calibration for Knowledge Graph Embedding Models
ICLR 2020
• Accenture/AmpliGraph
•
We show popular embedding models are indeed uncalibrated.
CALIBRATION FOR LINK PREDICTION KNOWLEDGE GRAPH EMBEDDING KNOWLEDGE GRAPHS
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.
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.
COMMUNITY DETECTION GRAPH CLASSIFICATION GRAPH EMBEDDING GRAPH MINING NODE CLASSIFICATION
GL2vec: Graph Embedding Enriched by Line Graphs with Edge Features
Specifically, it complements either the edge label information or the structural information which Graph2vec misses with the embeddings of the line graphs.