Community preserving network embedding
- Submitting institution
-
University of Greenwich
- Unit of assessment
- 11 - Computer Science and Informatics
- Output identifier
- 30501
- Type
- E - Conference contribution
- DOI
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-
- Title of conference / published proceedings
- Proceedings of the Thirty-First AAAI Conference on Artificial Intelligence
- First page
- 203
- Volume
- 0
- Issue
- -
- ISSN
- -
- Open access status
- -
- Month of publication
- February
- 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
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5
- Research group(s)
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-
- Citation count
- 164
- Proposed double-weighted
- No
- Reserve for an output with double weighting
- No
- Additional information
- Network embedding is of paramount importance in many real applications such as biomedical data analysis. However, the mesoscopic structure (community), which is an important description of network structure, is largely ignored by existing methods, despite the community imposing high-level constraints onto the microscopic structure. Motivated by this, we proposed a novel M-NMF model, which not only preserves the microscopic structure but also preserves the mesoscopic structure by incorporating the community constraints, which outperforms state of the art approaches in terms of both clustering and classification. The acceptance rate at IJCAI 2017 was 26%.
- Author contribution statement
- -
- Non-English
- No
- English abstract
- -
