Graph Embedding

123 papers with code · Graphs

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

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.

DOCUMENT CLASSIFICATION GRAPH EMBEDDING GRAPH REGRESSION LINK PREDICTION NODE CLASSIFICATION SKELETON BASED ACTION RECOGNITION

PyTorch-BigGraph: A Large-scale Graph Embedding System

28 Mar 2019facebookresearch/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 LiveJournal (MRR metric)

GRAPH EMBEDDING GRAPH PARTITIONING LINK PREDICTION

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.

GRAPH EMBEDDING

struc2vec: Learning Node Representations from Structural Identity

11 Apr 2017shenweichen/GraphEmbedding

Implementation and experiments of graph embedding algorithms. deep walk, LINE(Large-scale Information Network Embedding), node2vec, SDNE(Structural Deep Network Embedding), struc2vec

GRAPH EMBEDDING NETWORK EMBEDDING NODE CLASSIFICATION

LINE: Large-scale Information Network Embedding

12 Mar 2015shenweichen/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.

GRAPH EMBEDDING LINK PREDICTION NETWORK EMBEDDING NODE CLASSIFICATION

Graph Embedding Techniques, Applications, and Performance: A Survey

8 May 2017palash1992/GEM

Graphs, such as social networks, word co-occurrence networks, and communication networks, occur naturally in various real-world applications.

GRAPH EMBEDDING

Asymmetric Transitivity Preserving Graph Embedding

‏‏‎ ‎ 2020 benedekrozemberczki/karateclub

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.

GRAPH EMBEDDING LINK PREDICTION

An API Oriented Open-source Python Framework for Unsupervised Learning on Graphs

arXiv 2020 benedekrozemberczki/karateclub

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 EMBEDDING

An API Oriented Open-source Python Framework for Unsupervised Learning on Graphs

arXiv 2020 benedekrozemberczki/karateclub

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