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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 141–150 of 403 papers

TitleStatusHype
A novel stochastic model based on echo state networks for hydrological time series forecasting—0
Community detection using low-dimensional network embedding algorithms—0
GANE: A Generative Adversarial Network Embedding—0
Community Aware Random Walk for Network Embedding—0
ActiveHNE: Active Heterogeneous Network Embedding—0
Genome Sequence Classification for Animal Diagnostics with Graph Representations and Deep Neural Networks—0
Grammar-Based Grounded Lexicon Learning—0
Collaborative filtering via heterogeneous neural networks—0
Complex Network Classification with Convolutional Neural Network—0
Explainable, Stable, and Scalable Graph Convolutional Networks for Learning Graph Representation—0
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