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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

Showing 261270 of 403 papers

TitleStatusHype
Network2Vec Learning Node Representation Based on Space Mapping in Networks0
Tutorial on NLP-Inspired Network Embedding0
RiWalk: Fast Structural Node Embedding via Role IdentificationCode0
Dynamic Embedding on Textual Networks via a Gaussian ProcessCode0
Improving Textual Network Learning with Variational Homophilic EmbeddingsCode0
Neural Embedding Propagation on Heterogeneous NetworksCode0
Multi-scale Attributed Node EmbeddingCode0
Selective Network Discovery via Deep Reinforcement Learning on Embedded Spaces0
Temporal Network Embedding with Micro- and Macro-dynamicsCode0
HeteSpaceyWalk: A Heterogeneous Spacey Random Walk for Heterogeneous Information Network Embedding0
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