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http://bura.brunel.ac.uk/handle/2438/32461| Title: | Intelligent Community Detection: Review |
| Authors: | Moosa, J Awad, W Kalganova, T |
| Keywords: | intelligent community;graph mining;community detection |
| Issue Date: | 10-Feb-2020 |
| Citation: | Moosa, J., Awad, W. and Kalganova, T. (2020) 'Intelligent Community Detection: Review', 11th Annual PhD without Residence Doctoral Symposium (PwR - 2020), Manama, Bahrain, 10-11 February, pp. 1 - 7. |
| Abstract: | An emerging multidisciplinary field nowadays is graph mining; which is basically a form of data mining that deals with graphs instead of normal data, (Cook & Holder, 2006) it intends to discover repetitive sub-graphs and interesting patterns that occurs in the input graph. This research is focusing on community detection using graph mining techniques; the objectives are to study the existing methods used to solve this problem by comparing the evaluation parameters, and then develop a method for community detection in social networks, which meets the criteria identified for assessing a good algorithm. Various techniques of graph mining will be tested to determine wither it can meet the identified criteria. |
| Description: | A recording of the conference paper presented by Jenan Moosa at the 11th Annual PhD without Residence Doctoral Symposium (PwR - 2020), Manama, Bahrain, 10-11 February 2020 is available on YouTube at: https://www.youtube.com/watch?v=oIi69hA8x2Y . A copy of the paper is available online at SSRN: https://ssrn.com/abstract=3659107 . |
| URI: | https://bura.brunel.ac.uk/handle/2438/32461 |
| Other Identifiers: | ORCiD: Tatiana Kalganova https://orcid.org/0000-0003-4859-7152 |
| Appears in Collections: | Dept of Electronic and Electrical Engineering Research Papers |
Files in This Item:
| File | Description | Size | Format | |
|---|---|---|---|---|
| FullText.pdf | 573.35 kB | Adobe PDF | View/Open |
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