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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 71–80 of 403 papers

TitleStatusHype
Deep Learning for Learning Graph Representations—0
Author Name Disambiguation via Heterogeneous Network Embedding from Structural and Semantic Perspectives—0
ALPINE: Active Link Prediction using Network Embedding—0
Adversarial Attacks on Deep Graph Matching—0
Attributed Network Embedding Model for Exposing COVID-19 Spread Trajectory Archetypes—0
Attributed Network Embedding for Learning in a Dynamic Environment—0
Aligning Users Across Social Networks Using Network Embedding—0
AAANE: Attention-based Adversarial Autoencoder for Multi-scale Network Embedding—0
Deep Partial Multiplex Network Embedding—0
Diffusion Based Network Embedding—0
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