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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 391–400 of 403 papers

TitleStatusHype
MS-IMAP -- A Multi-Scale Graph Embedding Approach for Interpretable Manifold Learning—0
Multi-Aspect Temporal Network Embedding: A Mixture of Hawkes Process View—0
Multi-Hot Compact Network Embedding—0
Multimodal Deep Network Embedding with Integrated Structure and Attribute Information—0
Multi Objective Resource Optimization of Wireless Network Based on Cross Domain Virtual Network Embedding—0
Multi-Vector Embedding on Networks with Taxonomies—0
Multi-View Dynamic Heterogeneous Information Network Embedding—0
Multi-View Network Embedding Via Graph Factorization Clustering and Co-Regularized Multi-View Agreement—0
MUSE: Multi-faceted Attention for Signed Network Embedding—0
NECA: Network-Embedded Deep Representation Learning for Categorical Data—0
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