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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
Citation: 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

Evaluating Group Formation in Virtual Communities

doi: 10.1109/JAS.2020.1003237
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  • 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.

     

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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.

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