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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
Cross-Network Social User Embedding with Hybrid Differential Privacy GuaranteesCode0
Associative Learning for Network Embedding—0
Signed Network Embedding with Application to Simultaneous Detection of Communities and Anomalies—0
Large-Scale Privacy-Preserving Network Embedding against Private Link Inference Attacks—0
NECA: Network-Embedded Deep Representation Learning for Categorical Data—0
Deep Partial Multiplex Network Embedding—0
Pay Attention to Relations: Multi-embeddings for Attributed Multiplex Networks—0
Grammar-Based Grounded Lexicon Learning—0
Taking a Step Back with KCal: Multi-Class Kernel-Based Calibration for Deep Neural Networks—0
Learning Asymmetric Embedding for Attributed Networks via Convolutional Neural Network—0
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