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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 391–400 of 403 papers

TitleStatusHype
Where are we in embedding spaces? A Comprehensive Analysis on Network Embedding Approaches for Recommender SystemsCode0
Vertex-reinforced Random Walk for Network EmbeddingCode0
Vaccine skepticism detection by network embeddingCode0
MGCN: Semi-supervised Classification in Multi-layer Graphs with Graph Convolutional NetworksCode0
Multi-Level Network Embedding with Boosted Low-Rank Matrix ApproximationCode0
Relation order histograms as a network embedding toolCode0
Splitter: Learning Node Representations that Capture Multiple Social ContextsCode0
CSNE: Conditional Signed Network EmbeddingCode0
Cross-Network Social User Embedding with Hybrid Differential Privacy GuaranteesCode0
Multi-Relation Aware Temporal Interaction Network EmbeddingCode0
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