Attributed Network Embedding via Subspace Discovery

Data Mining and Knowledge Discovery, pp. 1953-1980, 2019.

Cited by: 5|Bibtex|Views54|DOI:https://doi.org/10.1007/s10618-019-00650-2
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Other Links: dblp.uni-trier.de|academic.microsoft.com|arxiv.org|link.springer.com

Abstract:

Network embedding aims to learn a latent, low-dimensional vector representations of network nodes, effective in supporting various network analytic tasks. While prior arts on network embedding focus primarily on preserving network topology structure to learn node representations, recently proposed attributed network embedding algorithms a...More

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