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Network Embedding

Network Embedding, also known as "Network Representation Learning", is a collective term for techniques for mapping graph nodes to vectors of real numbers in a multidimensional space. To be useful, a good embedding should preserve the structure of the graph. The vectors can then be used as input to various network and graph analysis tasks, such as link prediction

Source: Tutorial on NLP-Inspired Network Embedding

Papers

Showing 8190 of 403 papers

TitleStatusHype
Data driven approximation of parametrized PDEs by Reduced Basis and Neural NetworksCode0
DANES: Deep Neural Network Ensemble Architecture for Social and Textual Context-aware Fake News DetectionCode0
Adversarial Attack on Network Embeddings via Supervised Network PoisoningCode0
GARG-AML against Smurfing: A Scalable and Interpretable Graph-Based Framework for Anti-Money LaunderingCode0
CSNE: Conditional Signed Network EmbeddingCode0
DeBayes: a Bayesian Method for Debiasing Network EmbeddingsCode0
A Simple and Powerful Framework for Stable Dynamic Network EmbeddingCode0
GENE: Global Event Network EmbeddingCode0
Flexible Attributed Network EmbeddingCode0
Cross-Network Social User Embedding with Hybrid Differential Privacy GuaranteesCode0
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