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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 311–320 of 403 papers

TitleStatusHype
Diffusion Maps for Textual Network Embedding—0
DINE: A Framework for Deep Incomplete Network Embedding—0
DISCO: Influence Maximization Meets Network Embedding and Deep Learning—0
Document Network Embedding: Coping for Missing Content and Missing Links—0
Document Network Projection in Pretrained Word Embedding Space—0
Dynamic Graph Embedding via LSTM History Tracking—0
Dynamic Network Embeddings for Network Evolution Analysis—0
Dynamic Network Embedding Survey—0
Dynamic Virtual Network Embedding Algorithm based on Graph Convolution Neural Network and Reinforcement Learning—0
dynnode2vec: Scalable Dynamic Network Embedding—0
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