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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
Large-scale Gender/Age Prediction of Tumblr Users—0
Deep Learning for Learning Graph Representations—0
LouvainNE: Hierarchical Louvain Method for High Quality and Scalable Network Embedding.Code1
A Non-negative Symmetric Encoder-Decoder Approach for Community Detection—0
Privacy Attacks on Network Embeddings—0
Beyond Node Embedding: A Direct Unsupervised Edge Representation Framework for Homogeneous Networks—0
Document Network Embedding: Coping for Missing Content and Missing Links—0
JNET: Learning User Representations via Joint Network Embedding and Topic EmbeddingCode0
MANELA: A Multi-Agent Algorithm for Learning Network Embeddings—0
BHIN2vec: Balancing the Type of Relation in Heterogeneous Information Network—0
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