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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 251–260 of 403 papers

TitleStatusHype
AHINE: Adaptive Heterogeneous Information Network Embedding—0
Aligning context-based statistical models of language with brain activity during reading—0
Aligning Users Across Social Networks Using Network Embedding—0
ALPINE: Active Link Prediction using Network Embedding—0
A Model-data-driven Network Embedding Multidimensional Features for Tomographic SAR Imaging—0
A multi-domain virtual network embedding algorithm with delay prediction—0
A Multi-Domain VNE Algorithm based on Load Balancing in the IoT networks—0
A multi-domain VNE algorithm based on multi-objective optimization for IoD architecture in Industry 4.0—0
A Multi-Semantic Metapath Model for Large Scale Heterogeneous Network Representation Learning—0
A Node Embedding Framework for Integration of Similarity-based Drug Combination Prediction—0
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