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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 101–110 of 403 papers

TitleStatusHype
Vaccine skepticism detection by network embeddingCode0
A novel stochastic model based on echo state networks for hydrological time series forecasting—0
CoarSAS2hvec: Heterogeneous Information Network Embedding with Balanced Network Sampling—0
Multi-Relation Aware Temporal Interaction Network EmbeddingCode0
Reinforcement Learning for Admission Control in Wireless Virtual Network Embedding—0
Latent Network Embedding via Adversarial Auto-encoders—0
Neurally boosted supervised spectral clustering—0
Multi-Vector Embedding on Networks with Taxonomies—0
Scalable Hierarchical Embeddings of Complex Networks—0
Network representation learning systematic review: ancestors and current development state—0
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