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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 91–100 of 403 papers

TitleStatusHype
Attribute2vec: Deep Network Embedding Through Multi-Filtering GCN—0
Learning to Embed Categorical Features without Embedding Tables for Recommendation—0
DeepHE: Accurately Predicting Human Essential Genes based on Deep Learning—0
Deep Hashing for Signed Social Network Embedding—0
Data-driven biological network alignment that uses topological, sequence, and functional information—0
Deep Learning for Learning Graph Representations—0
Attributed Network Embedding Model for Exposing COVID-19 Spread Trajectory Archetypes—0
Aligning Users Across Social Networks Using Network Embedding—0
Deep Partial Multiplex Network Embedding—0
AHINE: Adaptive Heterogeneous Information Network Embedding—0
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