A novel granular approach for detecting dynamic online communities in social network
- Submitting institution
-
Goldsmiths' College
- Unit of assessment
- 11 - Computer Science and Informatics
- Output identifier
- 3125
- Type
- D - Journal article
- DOI
-
10.1007/s00500-018-3585-z
- Title of journal
- Soft Computing
- Article number
- -
- First page
- 10339
- Volume
- 23
- Issue
- 20
- ISSN
- 1432-7643
- Open access status
- Deposit exception
- Month of publication
- October
- Year of publication
- 2018
- URL
-
http://research.gold.ac.uk/id/eprint/27059/
- 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
-
3
- Research group(s)
-
-
- Citation count
- 0
- Proposed double-weighted
- No
- Reserve for an output with double weighting
- No
- Additional information
- Social networks have been rapidly growing in recent years. There are many community detection algorithms in use today, however, most of these algorithms are designed to discover communities in static networks and do not scale well, while networks today are continually changing their structure. This work is significant because it proposes a novel way to detect online dynamic communities in social networks, capable of detecting both low and abrupt changes in the network. Detection of online communities provides a valuable insight in many application domains including crime detection, disease spread and many more.
- Author contribution statement
- -
- Non-English
- No
- English abstract
- -