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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 271–280 of 403 papers

TitleStatusHype
Attributed Network Embedding Model for Exposing COVID-19 Spread Trajectory Archetypes—0
Author Name Disambiguation via Heterogeneous Network Embedding from Structural and Semantic Perspectives—0
A Weakly Supervised Segmentation Network Embedding Cross-scale Attention Guidance and Noise-sensitive Constraint for Detecting Tertiary Lymphoid Structures of Pancreatic Tumors—0
Barlow Graph Auto-Encoder for Unsupervised Network Embedding—0
Beyond Node Embedding: A Direct Unsupervised Edge Representation Framework for Homogeneous Networks—0
BHIN2vec: Balancing the Type of Relation in Heterogeneous Information Network—0
Bib2vec: Embedding-based Search System for Bibliographic Information—0
Big Networks: A Survey—0
Broad Learning for Healthcare—0
Can Network Embedding of Distributional Thesaurus be Combined with Word Vectors for Better Representation?—0
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