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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 351–360 of 403 papers

TitleStatusHype
Diffusion Maps for Textual Network Embedding—0
GANE: A Generative Adversarial Network Embedding—0
Diffusion Based Network Embedding—0
Billion-scale Network Embedding with Iterative Random ProjectionCode0
RSDNE: Exploring Relaxed Similarity and Dissimilarity from Completely-imbalanced Labels for Network EmbeddingCode0
Neural-Brane: Neural Bayesian Personalized Ranking for Attributed Network EmbeddingCode0
Scalable attribute-aware network embedding with locality—0
Fusing Structure and Content via Non-negative Matrix Factorization for Embedding Information NetworksCode0
Learning Depth from Single Images with Deep Neural Network Embedding Focal Length—0
AAANE: Attention-based Adversarial Autoencoder for Multi-scale Network Embedding—0
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