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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

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Showing 51–60 of 403 papers

TitleStatusHype
Zoo Guide to Network Embedding—0
H^2TNE: Temporal Heterogeneous Information Network Embedding in Hyperbolic SpacesCode0
Space-Invariant Projection in Streaming Network Embedding—0
DANES: Deep Neural Network Ensemble Architecture for Social and Textual Context-aware Fake News DetectionCode0
Learning Semantic Relationship Among Instances for Image-Text MatchingCode1
Cross Version Defect Prediction with Class Dependency Embeddings—0
Author Name Disambiguation via Heterogeneous Network Embedding from Structural and Semantic Perspectives—0
Recommending on graphs: a comprehensive review from a data perspective—0
A Model-data-driven Network Embedding Multidimensional Features for Tomographic SAR Imaging—0
An Isolation-Aware Online Virtual Network Embedding via Deep Reinforcement Learning—0
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