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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

Showing 361370 of 403 papers

TitleStatusHype
BiasedWalk: Biased Sampling for Representation Learning on GraphsCode0
SHINE: Signed Heterogeneous Information Network Embedding for Sentiment Link PredictionCode0
A Simple and Powerful Framework for Stable Dynamic Network EmbeddingCode0
ASD Classification on Dynamic Brain Connectome using Temporal Random Walk with Transformer-based Dynamic Network EmbeddingCode0
TempNodeEmb:Temporal Node Embedding considering temporal edge influence matrixCode0
LEAP nets for power grid perturbationsCode0
Temporal Graph Network Embedding with Causal Anonymous Walks RepresentationsCode0
Unsupervised Network Embedding for Graph Visualization, Clustering and ClassificationCode0
Learning Deep Network Representations with Adversarially Regularized AutoencodersCode0
Temporal Network Embedding with Micro- and Macro-dynamicsCode0
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