Generic, network schema agnostic sparse tensor factorization for single-pass clustering of heterogeneous information networks

Heterogeneous information networks (e.g. bibliographic networks and social media networks) that consist of multiple interconnected objects are ubiquitous. Clustering analysis is an effective method to understand the semantic information and interpretable structure of the heterogeneous information ne...

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Bibliographic Details

Main Authors:	Jibing Wu, Qinggang Meng, Su Deng, Hongbin Huang, Yahui Wu, Atta Badii
Format:	Default Article
Published:	2017
Subjects:	Other information and computing sciences not elsewhere classified untagged Information and Computing Sciences not elsewhere classified
Online Access:	https://hdl.handle.net/2134/24446
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