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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

Showing 321330 of 403 papers

TitleStatusHype
Deep Network Embedding for Graph Representation Learning in Signed NetworksCode0
COSINE: Compressive Network Embedding on Large-scale Information Networks0
Learning Features of Network Structures Using Graphlets0
LNEMLC: Label Network Embeddings for Multi-Label ClassificationCode0
dynnode2vec: Scalable Dynamic Network Embedding0
Enhanced Network Embedding with Text InformationCode0
Attributed Network Embedding for Incomplete Attributed NetworksCode0
Flexible Attributed Network EmbeddingCode0
MGCN: Semi-supervised Classification in Multi-layer Graphs with Graph Convolutional NetworksCode0
SepNE: Bringing Separability to Network Embedding0
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