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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 291–300 of 403 papers

TitleStatusHype
DANE: Domain Adaptive Network EmbeddingCode1
Is a Single Vector Enough? Exploring Node Polysemy for Network EmbeddingCode0
Relation Structure-Aware Heterogeneous Information Network Embedding—0
ActiveHNE: Active Heterogeneous Network Embedding—0
Representation Learning for Attributed Multiplex Heterogeneous NetworkCode1
Physiological Signal Embeddings (PHASE) via Interpretable Stacked Models—0
ExplaiNE: An Approach for Explaining Network Embedding-based Link Predictions—0
Tag2Vec: Learning Tag Representations in Tag Networks—0
Compositional Network Embedding—0
Data driven approximation of parametrized PDEs by Reduced Basis and Neural NetworksCode0
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