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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 181–190 of 403 papers

TitleStatusHype
A Block-based Generative Model for Attributed Networks Embedding—0
Document Network Embedding: Coping for Missing Content and Missing Links—0
DISCO: Influence Maximization Meets Network Embedding and Deep Learning—0
DINE: A Framework for Deep Incomplete Network Embedding—0
Beyond Node Embedding: A Direct Unsupervised Edge Representation Framework for Homogeneous Networks—0
A multi-domain virtual network embedding algorithm with delay prediction—0
Diffusion Maps for Textual Network Embedding—0
Diffusion Based Network Embedding—0
Barlow Graph Auto-Encoder for Unsupervised Network Embedding—0
Detecting Online Hate Speech: Approaches Using Weak Supervision and Network Embedding Models—0
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