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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 211–220 of 403 papers

TitleStatusHype
Big Networks: A Survey—0
DINE: A Framework for Deep Incomplete Network Embedding—0
Detecting Online Hate Speech: Approaches Using Weak Supervision and Network Embedding Models—0
Genome Sequence Classification for Animal Diagnostics with Graph Representations and Deep Neural Networks—0
Integrating Network Embedding and Community Outlier Detection via Multiclass Graph DescriptionCode0
A Multi-Semantic Metapath Model for Large Scale Heterogeneous Network Representation Learning—0
Next Waves in Veridical Network Embedding—0
SCE: Scalable Network Embedding from Sparsest CutCode0
Online Dynamic Network Embedding—0
EPARS: Early Prediction of At-risk Students with Online and Offline Learning Behaviors—0
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