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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
Heterogeneous Network Embedding for Deep Semantic Relevance Match in E-commerce Search—0
HeteSpaceyWalk: A Heterogeneous Spacey Random Walk for Heterogeneous Information Network Embedding—0
Hierarchical Graph Neural Networks—0
High-order joint embedding for multi-level link prediction—0
High Tension Lines: Predicting robustness of high-voltage power-grids to cascading failure using network embedding—0
Homogeneous Network Embedding for Massive Graphs via Reweighted Personalized PageRank—0
HONEM: Learning Embedding for Higher Order Networks—0
Hyperbolic Multiplex Network Embedding with Maps of Random Walk—0
Hyperbolic Node Embedding for Signed Networks—0
Identifying Transition States of Chemical Kinetic Systems using Network Embedding Techniques—0
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