Identifying communities of trust and confidence in the charity and not-for-profit sector: A memetic algorithm approach

Naeni, LM; De Vries, NJ; Reis, R; Arefin, AS; Berretta, R; Moscato, P

Identifying communities of trust and confidence in the charity and not-for-profit sector: A memetic algorithm approach

Naeni, LM

De Vries, NJ Reis, R Arefin, AS Berretta, R Moscato, P

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Publication Type:: Conference Proceeding
Citation:: Proceedings - 4th IEEE International Conference on Big Data and Cloud Computing, BDCloud 2014 with the 7th IEEE International Conference on Social Computing and Networking, SocialCom 2014 and the 4th International Conference on Sustainable Computing and Communications, SustainCom 2014, 2014, pp. 500 - 507
Issue Date:: 2014-01-01

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Field	Value	Language
dc.contributor.author	Naeni, LM https://orcid.org/0000-0002-3360-7680	en_US
dc.contributor.author	De Vries, NJ	en_US
dc.contributor.author	Reis, R	en_US
dc.contributor.author	Arefin, AS	en_US
dc.contributor.author	Berretta, R	en_US
dc.contributor.author	Moscato, P	en_US
dc.date.issued	2014-01-01	en_US
dc.identifier.citation	Proceedings - 4th IEEE International Conference on Big Data and Cloud Computing, BDCloud 2014 with the 7th IEEE International Conference on Social Computing and Networking, SocialCom 2014 and the 4th International Conference on Sustainable Computing and Communications, SustainCom 2014, 2014, pp. 500 - 507	en_US
dc.identifier.isbn	9781479967193	en_US
dc.identifier.uri	http://hdl.handle.net/10453/120630
dc.description.abstract	© 2014 IEEE. In this study we analyse complete networks derived from field survey and market research through proposing an efficient methodology based on proximity graphs and clustering techniques enhanced with a new community detection algorithm. The specific context is the charity and Not-For-Profit sector in Australia and consumer behaviours within this context. To investigate the performance of this methodology we conduct experiments on the network extracted from a dataset that contains responses of 1,550 individual Australians to 43 questions in a quantitative survey conducted on behalf of the Australian Charities and Not-for-Profits Commission to study the public trust and confidence in Australian charities. Here, we generate the distance matrix by computing the Spearman correlation coefficient as a similarity metric among individuals. Then, several types of k-Nearest Neighbour (kNN) graphs were calculated from the distance matrix and the new community detection algorithm detected groups of consumers by optimizing a quality function called 'modularity'. Comparison of obtained results with the results of the BGLL algorithm, a heuristic given by the publicly available package Gephi and the MST-kNN algorithm, a graph-based approach to compute clusters that has several applications in bioinformatics and finance, reveals that our methodology is effective in partitioning of complete graphs and detecting communities. The combined results indicate that behavioural models that investigate trust in charities may need to be aware of intrinsic differences among subgroups as revealed by our analysis.	en_US
dc.relation.ispartof	Proceedings - 4th IEEE International Conference on Big Data and Cloud Computing, BDCloud 2014 with the 7th IEEE International Conference on Social Computing and Networking, SocialCom 2014 and the 4th International Conference on Sustainable Computing and Communications, SustainCom 2014	en_US
dc.relation.isbasedon	10.1109/BDCloud.2014.83	en_US
dc.title	Identifying communities of trust and confidence in the charity and not-for-profit sector: A memetic algorithm approach	en_US
dc.type	Conference Proceeding
utslib.for	0806 Information Systems	en_US
pubs.embargo.period	Not known	en_US
pubs.organisational-group	/University of Technology Sydney
pubs.organisational-group	/University of Technology Sydney/Faculty of Design, Architecture and Building
pubs.organisational-group	/University of Technology Sydney/Faculty of Design, Architecture and Building/School of Built Environment
utslib.copyright.status	closed_access
pubs.publication-status	Published	en_US

Abstract:

© 2014 IEEE. In this study we analyse complete networks derived from field survey and market research through proposing an efficient methodology based on proximity graphs and clustering techniques enhanced with a new community detection algorithm. The specific context is the charity and Not-For-Profit sector in Australia and consumer behaviours within this context. To investigate the performance of this methodology we conduct experiments on the network extracted from a dataset that contains responses of 1,550 individual Australians to 43 questions in a quantitative survey conducted on behalf of the Australian Charities and Not-for-Profits Commission to study the public trust and confidence in Australian charities. Here, we generate the distance matrix by computing the Spearman correlation coefficient as a similarity metric among individuals. Then, several types of k-Nearest Neighbour (kNN) graphs were calculated from the distance matrix and the new community detection algorithm detected groups of consumers by optimizing a quality function called 'modularity'. Comparison of obtained results with the results of the BGLL algorithm, a heuristic given by the publicly available package Gephi and the MST-kNN algorithm, a graph-based approach to compute clusters that has several applications in bioinformatics and finance, reveals that our methodology is effective in partitioning of complete graphs and detecting communities. The combined results indicate that behavioural models that investigate trust in charities may need to be aware of intrinsic differences among subgroups as revealed by our analysis.

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http://hdl.handle.net/10453/120630