Graph Embedding
Graph embeddings learn a mapping from a network to a vector space, while preserving relevant network properties.
( Image credit: GAT )
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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.
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
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.
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
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.
#5 best model for
Node Classification
on Wikipedia
GRAPH EMBEDDING LINK PREDICTION NETWORK EMBEDDING NODE CLASSIFICATION
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
• 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.
#11 best model for
Graph Classification
on NCI109
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.
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 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.