Generic, network schema agnostic sparse tensor factorization for single-pass clustering of heterogeneous information networks
- Submitting institution
-
The University of Reading
- Unit of assessment
- 11 - Computer Science and Informatics
- Output identifier
- 69359
- Type
- D - Journal article
- DOI
-
10.1371/journal.pone.0172323
- Title of journal
- PLoS ONE
- Article number
- e0172323
- First page
- -
- Volume
- 12
- Issue
- 2
- ISSN
- 1932-6203
- Open access status
- Compliant
- Month of publication
- -
- Year of publication
- 2017
- URL
-
-
- Supplementary information
-
-
- Request cross-referral to
- -
- Output has been delayed by COVID-19
- No
- COVID-19 affected output statement
- -
- Forensic science
- No
- Criminology
- No
- Interdisciplinary
- No
- Number of additional authors
-
5
- Research group(s)
-
8 - CV
- Citation count
- 1
- Proposed double-weighted
- No
- Reserve for an output with double weighting
- No
- Additional information
- Scalable efficient single-pass clustering of semantics structures within heterogenous information networks represents an increasingly compute-intensive and challenging task given the complexity and variety of the network schema that can be encountered and the relentless proliferation of information networks such as the social media networks in an age of increasing hyper-connectivity and Big Data. The significance of this paper is that it presents a novel clustering framework based on sparse tensor factorisation and exploits the expressive power of tucker decomposition to optimise the clustering of multiple types simultaneously with a single-pass schema-agnostic capability.
- Author contribution statement
- -
- Non-English
- No
- English abstract
- -
