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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 231–240 of 403 papers

TitleStatusHype
DeBayes: a Bayesian Method for Debiasing Network EmbeddingsCode0
A Node Embedding Framework for Integration of Similarity-based Drug Combination Prediction—0
Benchmarking Network Embedding Models for Link Prediction: Are We Making Progress?Code0
Using Distributional Thesaurus Embedding for Co-hyponymy Detection—0
FONDUE: A Framework for Node Disambiguation Using Network Embeddings—0
DeepHE: Accurately Predicting Human Essential Genes based on Deep Learning—0
Vertex-reinforced Random Walk for Network EmbeddingCode0
ALPINE: Active Link Prediction using Network Embedding—0
Data-driven biological network alignment that uses topological, sequence, and functional information—0
Document Network Projection in Pretrained Word Embedding Space—0
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