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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 71–80 of 403 papers

TitleStatusHype
NECA: Network-Embedded Deep Representation Learning for Categorical Data—0
Unsupervised Network Embedding Beyond HomophilyCode1
Deep Partial Multiplex Network Embedding—0
Pay Attention to Relations: Multi-embeddings for Attributed Multiplex Networks—0
Monkey Business: Reinforcement learning meets neighborhood search for Virtual Network EmbeddingCode1
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
A multi-domain VNE algorithm based on multi-objective optimization for IoD architecture in Industry 4.0—0
A Multi-Domain VNE Algorithm based on Load Balancing in the IoT networks—0
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