Evaluating Group Formation in Virtual Communities

Giancarlo Fortino; Antonio Liotta; Fabrizio Messina; Domenico Rosaci; Giuseppe M. L. Sarnè

doi:10.1109/JAS.2020.1003237

Volume 7 Issue 4

Jun. 2020

IEEE/CAA Journal of Automatica Sinica

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Giancarlo Fortino, Antonio Liotta, Fabrizio Messina, Domenico Rosaci and Giuseppe M. L. Sarnè, "Evaluating Group Formation in Virtual Communities," IEEE/CAA J. Autom. Sinica, vol. 7, no. 4, pp. 1003-1015, July 2020. doi: 10.1109/JAS.2020.1003237

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Giancarlo Fortino, Antonio Liotta, Fabrizio Messina, Domenico Rosaci and Giuseppe M. L. Sarnè, "Evaluating Group Formation in Virtual Communities," IEEE/CAA J. Autom. Sinica, vol. 7, no. 4, pp. 1003-1015, July 2020. doi: 10.1109/JAS.2020.1003237

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Evaluating Group Formation in Virtual Communities

doi: 10.1109/JAS.2020.1003237

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Author Bio:
Giancarlo Fortino is a Professor of computer engineering at the DIMES Department of the University of Calabria, Italy. He received the Ph.D. degree in computer engineering from Unical, in 1995 and 2000, respectively. He is also Adjunct Professor at Wuhan University of Technology and senior Research Fellow at the Italian National Research Council ICAR Institute, Italy. His research interests include agent-based computing, wireless (body) sensor networks, and Internet of Things. He is author of over 350 papers in international journals, conferences, and books. He is Co-Founder and CEO of SenSysCal S.r.l., a Unical spinoff focused on innovative IoT systems. He is currently Member of the IEEE SMCS BoG, and Chair of the IEEE SMCS Italian Chapter

Antonio Liotta is a Professor of data science and intelligent systems at the School of Computing, Edinburgh Napier University, U.K. His team is at the forefront of influential research in data science and artificial intelligence, specifically in the context of smart cities, Internet of Things, and smart sensing. He is renowned for his contributions to miniaturized machine learning, particularly in the context of the Internet of Things. He has led the international team that has recently made a breakthrough in artificial neural networks, using network science to accelerate the training process. Antonio is the Editor-in-Chief of the Springer Internet of Things book series and Associate Editor of many other high-profile journals

Fabrizio Messina received the Ph.D. degree in computer science from the Department of Mathematics and Informatics, University of Catania, Italy. He is an Assistant Professor at the Department of Mathematics and Informatics of the University of Catania. He has authored over 80 papers in international conferences and journals. His current research interests include cloud and grid computing, trust and recommender systems, complex systems, and simulation systems

Domenico Rosaci is an Associated Professor of computer engineering at the DIIES Department of the University of Reggio Calabria, Italy. He received the Ph.D. degree in electronic engineering in 1999. He is the Associate Editor of Computer Journal. His research interests mainly include distributed artificial intelligence, and in particular the intelligent agents, recommender systems, trust and reputation in social environments

Giuseppe M. L. Sarnè is an Assistant Professor of computer science at the Department DICEAM at the University Mediterranea of Reggio Calabria, Italy. His research interests mainly include distributed artificial intelligence, multi-agent systems, trust and reputation systems. He is a Member of a number of conference PCs and he is the Associate Editor of E-Commerce Research and Applications (Elsevier) and Big Data and Cognitive Computing (MDPI)
Corresponding author: F. Messina is with the Department of Mathematics and Informatics, University of Catania, Catania 95125, Italy (e-mail: messina@dmi.unict.it)
¹ www.facebook.com
² www.twitter.com
³ www.ciao.com
⁴ www.epinions.com
⁵ Data used in our experiments are publicly available at http://www.cse.msu.edu/~tangjili/trust.html
Received Date: 2020-03-25
Accepted Date: 2020-04-13

Available Online: 2020-05-14

Abstract

Abstract

In this paper, we are interested in answering the following research question: “Is it possible to form effective groups in virtual communities by exploiting trust information without significant overhead, similarly to real user communities?” In order to answer this question, instead of adopting the largely used approach of exploiting the opinions provided by all the users of the community (called global reputation), we propose to use a particular form of reputation, called local reputation. We also propose an algorithm for group formation able to implement the proposed procedure to form effective groups in virtual communities. Another interesting question is how to measure the effectiveness of groups in virtual communities. To this aim we introduce the $G_k$ index in a measure of the effectiveness of the group formation. We tested our algorithm by realizing some experimental trials on real data from the real world EPINIONS and CIAO communities, showing the significant advantages of our procedure w.r.t. another prominent approach based on traditional global reputation.
- Group formation,
- helpfulness,
- online social communities,
- reputation,
- trust

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¹ www.facebook.com
² www.twitter.com
³ www.ciao.com
⁴ www.epinions.com
⁵ Data used in our experiments are publicly available at http://www.cse.msu.edu/~tangjili/trust.html

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Highlights

The problem of forming effective groups in virtual communities is addressed.
The proposed solution exploits trust information without significant overhead by adopting local reputation instead of global reputation.
An index to measure the effectiveness of group formation is introduced, as well as an algorithm to drive group formation as proof of concept.
Experimental trials performed on two data sets extracted from social networks have shown that the adoption of the proposed solution offer significant advantages.

Evaluating Group Formation in Virtual Communities

doi: 10.1109/JAS.2020.1003237

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