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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 281–290 of 403 papers

TitleStatusHype
Hedging carbon risk with a network approach—0
Clustering Molecular Energy Landscapes by Adaptive Network Embedding—0
CoANE: Modeling Context Co-occurrence for Attributed Network Embedding—0
CoarSAS2hvec: Heterogeneous Information Network Embedding with Balanced Network Sampling—0
Collaborative filtering via heterogeneous neural networks—0
Community Aware Random Walk for Network Embedding—0
Community detection using low-dimensional network embedding algorithms—0
Complex Network Classification with Convolutional Neural Network—0
Compositional Network Embedding—0
Exact Recovery of Community Structures Using DeepWalk and Node2vec—0
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