Stochastic Graphlet Embedding
- Submitting institution
-
University of Exeter
- Unit of assessment
- 11 - Computer Science and Informatics
- Output identifier
- 6405
- Type
- D - Journal article
- DOI
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10.1109/TNNLS.2018.2884700
- Title of journal
- IEEE Transactions on Neural Networks and Learning Systems
- Article number
- -
- First page
- 2369
- Volume
- 30
- Issue
- 8
- ISSN
- 2162-237X
- Open access status
- Deposit exception
- Month of publication
- December
- Year of publication
- 2018
- 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
-
1
- Research group(s)
-
-
- Citation count
- 1
- Proposed double-weighted
- No
- Reserve for an output with double weighting
- No
- Additional information
- We propose an explicit graph embedding technique by stochastic sampling of graphlets and hashing those graphlets using the interesting collision avoidance property of betweenness centrality. This technique has been used by some further methods published later (DOIs: 10.1007/s00521-019-04642-7, 10.1109/TNNLS.2020.3006738, 10.1016/j.patrec.2020.05.023 etc), one of them uses hierarchical organization of graph which has been proven to be effective to capture robust information. Funding came from the EU MSCA (665919). Invited talks were given at the IST, Austria; the ICDAR 2019, Australia; and the University of Bristol, United Kingdom.
- Author contribution statement
- -
- Non-English
- No
- English abstract
- -