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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
Heterogeneous Information Network Embedding for Meta Path based Proximity—0
Task-Guided and Path-Augmented Heterogeneous Network Embedding for Author IdentificationCode0
Pairwise FastText Classifier for Entity Disambiguation—0
Identity-sensitive Word Embedding through Heterogeneous Networks—0
A General Framework for Content-enhanced Network Representation Learning—0
Deep Coevolutionary Network: Embedding User and Item Features for Recommendation—0
Predict Anchor Links across Social Networks via an Embedding Approach—0
Aligning Users Across Social Networks Using Network Embedding—0
Structural Deep Network EmbeddingCode0
Video Tracking Using Learned Hierarchical Features—0
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