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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 241250 of 403 papers

TitleStatusHype
Network Embedding: An Overview0
Time-aware Gradient Attack on Dynamic Network Link Prediction0
Hyperbolic Multiplex Network Embedding with Maps of Random Walk0
RWNE: A Scalable Random-Walk-Based Network Embedding Framework with Personalized Higher-Order Proximity PreservedCode0
Unsupervised Attributed Multiplex Network EmbeddingCode0
On Network Embedding for Machine Learning on Road Networks: A Case Study on the Danish Road Network0
weg2vec: Event embedding for temporal networks0
Dynamic Graph Embedding via LSTM History Tracking0
News2vec: News Network Embedding with Subnode Information0
Hyperbolic Node Embedding for Signed Networks0
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