Concept Drift and Anomaly Detection in Graph Streams
- Submitting institution
-
University of Exeter
- Unit of assessment
- 11 - Computer Science and Informatics
- Output identifier
- 1802
- Type
- D - Journal article
- DOI
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10.1109/TNNLS.2018.2804443
- Title of journal
- IEEE Transactions on Neural Networks and Learning Systems
- Article number
- -
- First page
- 5592
- Volume
- 29
- Issue
- 11
- ISSN
- 2162-237X
- Open access status
- Deposit exception
- Month of publication
- March
- 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
-
2
- Research group(s)
-
-
- Citation count
- 6
- Proposed double-weighted
- No
- Reserve for an output with double weighting
- No
- Additional information
- This is a pioneering study, as acknowledged by the community (e.g. doi: 10.1109/TNNLS.2018.2886956) and has led to further 6 high-quality papers from our group (e.g. DOI: 10.1109/TNNLS.2019.2927301, 10.1109/IJCNN.2019.8852131). The idea of change detection on graph sequences also allowed us to kick start a research project with our collaborators in Toronto, focusing on epileptic seizures monitored through functional networks evolving over time (see doi: https://doi.org/10.1101/2020.12.03.409979)
- Author contribution statement
- -
- Non-English
- No
- English abstract
- -