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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 211–220 of 403 papers

TitleStatusHype
REFINE: Random RangE FInder for Network Embedding—0
Reinforcement Learning for Admission Control in Wireless Virtual Network Embedding—0
Relation Structure-Aware Heterogeneous Information Network Embedding—0
Representation Learning for Recommender Systems with Application to the Scientific Literature—0
Representation Learning for Scale-free Networks—0
Resource-Efficient Neural Architect—0
Network Representation of Large-Scale Heterogeneous RNA Sequences with Integration of Diverse Multi-omics, Interactions, and Annotations Data—0
RNE: A Scalable Network Embedding for Billion-scale Recommendation—0
Scalable attribute-aware network embedding with locality—0
Scalable Hierarchical Embeddings of Complex Networks—0
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