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An optimisation tool for robust community detection algorithms using content and topology information

Bhih, A, Johnson, P and Randles, M (0019) An optimisation tool for robust community detection algorithms using content and topology information. Journal of Supercomputing, 76. pp. 226-254. ISSN 0920-8542

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Open Access URL: http://link.springer.com/article/10.1007/s11227-01... (Published version)


With the recent prevalence of information networks, the topic of community detection has gained much interest among researchers. In real-world networks, node attribute (content information) is also available in addition to topology information. However, the collected topology information for networks is usually noisy when there are missing edges. Furthermore, the existing community detection methods generally focus on topology information and largely ignore the content information. This makes the task of community detection for incomplete networks very challenging. A new method is proposed that seeks to address this issue and help improve the performance of the existing community detection algorithms by considering both sources of information, i.e. topology and content. Empirical results demonstrate that our proposed method is robust and can detect more meaningful community structures within networks having incomplete information, than the conventional methods that consider only topology information.

Item Type: Article
Uncontrolled Keywords: 0805 Distributed Computing, 0803 Computer Software
Subjects: Q Science > QA Mathematics
Q Science > QA Mathematics > QA75 Electronic computers. Computer science
T Technology > T Technology (General)
T Technology > TK Electrical engineering. Electronics. Nuclear engineering
Divisions: Electronics & Electrical Engineering (merged with Engineering 10 Aug 20)
Publisher: Springer US
Date Deposited: 24 Oct 2019 08:55
Last Modified: 04 Sep 2021 08:37
DOI or ID number: 10.1007/s11227-019-03018-x
URI: https://researchonline.ljmu.ac.uk/id/eprint/11619
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