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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 361–370 of 403 papers

TitleStatusHype
GPSP: Graph Partition and Space Projection based Approach for Heterogeneous Network EmbeddingCode0
AspEm: Embedding Learning by Aspects in Heterogeneous Information Networks—0
Range-Only Localization in n-Dimensional Networks With Arbitrary Anchor Placement—0
Can Network Embedding of Distributional Thesaurus be Combined with Word Vectors for Better Representation?—0
Learning Role-based Graph EmbeddingsCode0
Complex Network Classification with Convolutional Neural Network—0
mvn2vec: Preservation and Collaboration in Multi-View Network EmbeddingCode0
Learning Document Embeddings With CNNs—0
SHINE: Signed Heterogeneous Information Network Embedding for Sentiment Link PredictionCode0
PRUNE: Preserving Proximity and Global Ranking for Network EmbeddingCode0
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