Semanlink - Graph Embeddings

Parents :

Graph Embeddings

Traditionally, networks are usually represented as adjacency matrices. This suffers from data sparsity and high-dimensionality. Network embeddings aim to **represent network
vertices into a low-dimensional vector space, by preserving
both network topology structure and node content information**.

Algorithms are typically unsupervised
and can be broadly classified into
three groups ([source](/doc/2019/07/_1901_00596_a_comprehensive_su)):

- matrix factorization
- random walks 
- deep learning approaches (graph neural networks - GNNs)
	- graph convolution networks (GraphSage)
	- graph attention networks, 
	- graph auto-encoders (e.g., DNGR and SDNE)
	- graph generative networks, 
	- graph spatial-temporal networks.

Node embeddings (intuition: similar nodes should have similar vectors).

- Laplacian EigenMap (an eigenvector based computation, OK when matrix is not too large)
- LINE Large-scale Information Network Embedding, most cited paper at WWW2015; Breadth first search
- DeepWalk (Perozzi et al. 2014) (the technique to learn word embeddings adapted to nodes: treating nodes as words and generating short random walks as sentences)
- Node2Vec (2016) (mixed strategy)

etc.

Amis :

Graph neural networks

Descendants

24 Documents (Liste longue)

Graph representation learning in biomedicine and healthcare | Nature Biomedical Engineering

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2022-11-01 A propos

Understanding Graph Embeddings| by Dan McCreary | Nov, 2020 | Medium

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2020-12-06 A propos

Chris Mungall sur Twitter : "Reading: OWL2Vec*: Embedding of OWL Ontologies"

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2020-10-07 A propos

Graph Representation Learning Book - Will Hamilton

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2020-08-22 A propos

Bringing traditional ML to your Neo4j Graph with node2vec | Dave Voutila

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2020-08-06 A propos

[2003.08001] Realistic Re-evaluation of Knowledge Graph Completion Methods: An Experimental Study

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2020-05-15 A propos

[1807.08447] LinkNBed: Multi-Graph Representation Learning with Entity Linkage

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2020-05-11 A propos

Thomas Kipf's PhD thesis: "Deep Learning with Graph-Structured Representations"

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2020-05-05 A propos

[2004.06842] Layered Graph Embedding for Entity Recommendation using Wikipedia in the Yahoo! Knowledge Graph

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2020-04-17 A propos

Sebastian Ruder sur Twitter : network embeddings in the biomedical domain. @eurnlp #EurNLP2019

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2019-10-11 A propos

[1808.02590] A Tutorial on Network Embeddings

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2019-08-25 A propos

benedekrozemberczki/awesome-graph-classification: A collection of important graph embedding, classification and representation learning papers with implementations.

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2019-08-05 A propos

[1709.07604] A Comprehensive Survey of Graph Embedding: Problems, Techniques and Applications

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2019-05-29 A propos

PyTorch-BigGraph: Faster embeddings of large graphs - Facebook Code

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2019-05-12 A propos

International Workshop on Deep Learning for Graphs and Structured Data Embedding

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2019-04-30 A propos

Graph embedding Day - Lyon

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Graph Embeddings

2018-09-10 A propos

[1809.00782] Open Domain Question Answering Using Early Fusion of Knowledge Bases and Text

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2018-09-06 A propos

[1806.05662] GLoMo: Unsupervisedly Learned Relational Graphs as Transferable Representations

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2018-06-23 A propos

How do we capture structure in relational data?

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Graph Embeddings

2018-05-07 A propos

Convolutional Neural Networks on Graphs

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2018-05-05 A propos

TUTORIAL: Representation Learning on Networks - TheWebConf 2018

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Network representation learning (NRL): Approaches that automatically learn to encode network structure into low-dimensional embeddings, using techniques based on deep learning and nonlinear dimensionality reduction

**Goal of representation learning for networks: efficient task-independant feature learning for ML in networks.** But it's hard. DL toolbox are designed for single sequences or grids (for instance CNN for images, RNN or word2vec are fixed size), but networks are far more complex!

from the abstract:

> In this tutorial, we will cover key advancements in NRL over the last decade, with an emphasis on fundamental advancements made in the last two years. We will discuss classic matrix factorization-based methods (e.g., Laplacian eigenmaps), random-walk based algorithms (e.g., DeepWalk and node2vec), as well as very recent advancements in graph convolutional networks (GCNs). We will cover methods to embed individual nodes (see [node embeddings](/tag/node_embeddings)) as well as approaches to embed entire (sub)graphs, and in doing so, we will present a unified framework for NRL.

2018-05-05 A propos

[1603.08861] Revisiting Semi-Supervised Learning with Graph Embeddings

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2018-02-13 A propos

TUTORIAL: Representation Learning on Networks - TheWebConf 2018

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2018-01-27 A propos

WORKSHOP: BigNet @ WWW 2018 Workshop on Learning Representations for Big Networks

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2018-01-27 A propos

Propriétés

sl:creationDate : 2018-01-27
sl:creationTime : 2018-01-27T15:18:45Z
rdf:type : sl:Tag
skos:altLabel :
- Graph representation learning@en
- Representation Learning on Networks@en
- Network embeddings@en
- Network Representation Learning@en
skos:prefLabel : Graph Embeddings@en