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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 111–120 of 403 papers

TitleStatusHype
EPARS: Early Prediction of At-risk Students with Online and Offline Learning Behaviors—0
BHIN2vec: Balancing the Type of Relation in Heterogeneous Information Network—0
A Multi-Domain VNE Algorithm based on Load Balancing in the IoT networks—0
Dynamic Graph Embedding via LSTM History Tracking—0
AspEm: Embedding Learning by Aspects in Heterogeneous Information Networks—0
Dynamic Network Embedding Survey—0
Big Networks: A Survey—0
Dynamic Virtual Network Embedding Algorithm based on Graph Convolution Neural Network and Reinforcement Learning—0
dynnode2vec: Scalable Dynamic Network Embedding—0
Cross Version Defect Prediction with Class Dependency Embeddings—0
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