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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 81–90 of 403 papers

TitleStatusHype
L2G2G: a Scalable Local-to-Global Network Embedding with Graph AutoencodersCode0
Detecting local perturbations of networks in a latent hyperbolic embedding space—0
Clustering Molecular Energy Landscapes by Adaptive Network Embedding—0
BHGNN-RT: Network embedding for directed heterogeneous graphsCode0
Hedging carbon risk with a network approach—0
Semantic Annotation of Tabular Data for Machine-to-Machine Interoperability via Neuro-Symbolic Anchoring—0
A Simple and Powerful Framework for Stable Dynamic Network EmbeddingCode0
Trustworthiness-Driven Graph Convolutional Networks for Signed Network EmbeddingCode0
A Hybrid Membership Latent Distance Model for Unsigned and Signed Integer Weighted NetworksCode0
Network Embedding Using Sparse Approximations of Random Walks—0
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