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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 191200 of 403 papers

TitleStatusHype
Next Waves in Veridical Network Embedding0
Network Embedding with Completely-imbalanced LabelsCode1
Node Classification on Graphs with Few-Shot Novel Labels via Meta Transformed Network EmbeddingCode1
Online Dynamic Network Embedding0
SCE: Scalable Network Embedding from Sparsest CutCode0
Unsupervised Differentiable Multi-aspect Network EmbeddingCode1
EPARS: Early Prediction of At-risk Students with Online and Offline Learning Behaviors0
Network Together: Node Classification via Cross-Network Deep Network EmbeddingCode1
Integrated Node Encoder for Labelled Textual Networks0
CSNE: Conditional Signed Network EmbeddingCode0
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