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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
Name Disambiguation in Anonymized Graphs using Network EmbeddingCode0
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
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