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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
Adversarial Deep Network Embedding for Cross-network Node ClassificationCode1
A Block-based Generative Model for Attributed Networks Embedding—0
Compositional Network Embedding—0
Bib2vec: Embedding-based Search System for Bibliographic Information—0
A multi-domain VNE algorithm based on multi-objective optimization for IoD architecture in Industry 4.0—0
A Multi-Domain VNE Algorithm based on Load Balancing in the IoT networks—0
BHIN2vec: Balancing the Type of Relation in Heterogeneous Information Network—0
Big Networks: A Survey—0
A Multi-Semantic Metapath Model for Large Scale Heterogeneous Network Representation Learning—0
Adversarial Network Embedding—0
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