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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 161–170 of 403 papers

TitleStatusHype
Genome Sequence Classification for Animal Diagnostics with Graph Representations and Deep Neural Networks—0
COSINE: Compressive Network Embedding on Large-scale Information Networks—0
Learning Features of Network Structures Using Graphlets—0
Cross Version Defect Prediction with Class Dependency Embeddings—0
Collaborative filtering via heterogeneous neural networks—0
Explainable, Stable, and Scalable Graph Convolutional Networks for Learning Graph Representation—0
Associative Learning for Network Embedding—0
Grammar-Based Grounded Lexicon Learning—0
Unsupervised Graph Embedding via Adaptive Graph Learning—0
An Out-of-the-box Full-network Embedding for Convolutional Neural Networks—0
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