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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 221–230 of 403 papers

TitleStatusHype
Adversarial Deep Network Embedding for Cross-network Node ClassificationCode1
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
LouvainNE: Hierarchical Louvain Method for High Quality and Scalable Network EmbeddingCode1
Data-driven biological network alignment that uses topological, sequence, and functional information—0
Fast Sequence-Based Embedding with Diffusion GraphsCode1
Document Network Projection in Pretrained Word Embedding Space—0
Inductive Document Network Embedding with Topic-Word AttentionCode1
A Block-based Generative Model for Attributed Networks Embedding—0
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