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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 291–300 of 403 papers

TitleStatusHype
Controlled Deep Reinforcement Learning for Optimized Slice Placement—0
COSINE: Compressive Network Embedding on Large-scale Information Networks—0
Cross Version Defect Prediction with Class Dependency Embeddings—0
Data-driven biological network alignment that uses topological, sequence, and functional information—0
Deep Adversarial Network Alignment—0
Deep Coevolutionary Network: Embedding User and Item Features for Recommendation—0
Deep Contrastive Multiview Network Embedding—0
Deep Feature Learning of Multi-Network Topology for Node Classification—0
Learning to Embed Categorical Features without Embedding Tables for Recommendation—0
DeepHE: Accurately Predicting Human Essential Genes based on Deep Learning—0
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