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

JCR Impact Factor: 15.3, Top 1 (SCI Q1)

CiteScore: 23.5, Top 2% (Q1)
Google Scholar h5-index: 77， TOP 5

Turn off MathJax

Article Contents

Article Navigation > IEEE/CAA Journal of Automatica Sinica > 2020 > 7(4): 1003-1015

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

Citation:

PDF( 1413 KB)

Evaluating Group Formation in Virtual Communities

doi: 10.1109/JAS.2020.1003237

More Information

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

FullText(HTML)

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

References(58)

References

[1]	J. M. Leimeister, P. Sidiras, and H. Krcmar, “Success factors of virtual communities from the perspective of members and operators: an empirical study,” in Proc. 37th Annu. Hawaii Int. Conf. System Sciences, Big Island, USA, 2004, pp. 10.
[2]	M. Paldam, “Social capital: one or many? Definition and measurement” J. Econom. Surv., vol. 14, no. 5, pp. 629–653, Dec. 2000. doi: 10.1111/1467-6419.00127
[3]	K. Jérôme, “Konect: The koblenz network collection ,” in Proc. 22nd Int. Conf. World Wide Web, pp. 1343–1350, 2013.
[4]	G. Costa and R. Ortale, “Model-based collaborative personalized recommendation on signed social rating networks,” ACM Trans. Internet Technol., vol. 16, no. 3, pp. 20, Jul. 2016.
[5]	P. De Meo, K. Musial-Gabrys, D. Rosaci, G. M. L. Sarnè, and L. Aroyo, “Using centrality measures to predict helpfulness-based reputation in trust networks,” ACM Trans. Internet Technol., vol. 17, no. 1, pp. 8, Feb. 2017.
[6]	P. De Meo, F. Messina, D. Rosaci, and G. M. L. Sarnè, “Recommending users in social networks by integrating local and global reputation,” in Proc. 7th Int. Conf. Internet and Distributed Computing Systems, Calabria, Italy, 2014, pp. 437–446.
[7]	M. Eirinaki, M. D. Louta, and I. Varlamis, “A trust-aware system for personalized user recommendations in social networks,” IEEE Trans. Syst.,Man,Cybernet.:Syst., vol. 44, no. 4, pp. 409–421, Apr. 2014. doi: 10.1109/TSMC.2013.2263128
[8]	A. G. Sutcliffe, D. Wang, and R. I. M. Dunbar, “Modelling the role of trust in social relationships,” ACM Trans. Internet Technol., vol. 15, no. 4, pp. 16, Nov. 2015.
[9]	D. Rosaci, G. M. L. Sarnè, and S. Garruzzo, “Integrating trust measures in multiagent systems,” Int. J. Intell. Syst., vol. 27, no. 1, pp. 1–15, Jan. 2012. doi: 10.1002/int.20513
[10]	D. M. Kilgour and C. Eden, Handbook of Group Decision and Negotiation. Dordrecht, Europe, Springer, 2010.
[11]	P. Fishburn, “Arrow’s impossibility theorem: concise proof and infinite voters,” J. Econom. Theory, vol. 2, no. 1, pp. 103–106, Mar. 1970. doi: 10.1016/0022-0531(70)90015-3
[12]	P. De Meo, E. Ferrara, D. Rosaci, and G. M. L. Sarnè, “Trust and compactness in social network groups,” IEEE Trans. Cybernet., vol. 45, no. 2, pp. 205–216, Feb. 2015. doi: 10.1109/TCYB.2014.2323892
[13]	A. Jøsang, R. Ismail, and C. Boyd, “A survey of trust and reputation systems for online service provision,” Decis. Support Syst., vol. 43, no. 2, pp. 618–644, Mar. 2007. doi: 10.1016/j.dss.2005.05.019
[14]	L. S. Lai and E. Turban, “Groups formation and operations in the web 2.0 environment and social networks,” Group Decis. Negot., vol. 17, no. 5, pp. 387–402, Jun. 2008. doi: 10.1007/s10726-008-9113-2
[15]	J. L. Tang, X. Hu, H. J. Gao, and H. Liu, “Exploiting local and global social context for recommendation,” in Proc. Twenty-Third Int. Joint Conf. Artificial Intelligence, Beijing, China, 2013, pp. 2712–2718.
[16]	P. De Meo, F. Messina, D. Rosaci, and G. M. Sarnè, “Forming time-stable homogeneous groups into online social networks,” Inf. Sci., vol. 414, pp. 117–132, Nov. 2017. doi: 10.1016/j.ins.2017.05.048
[17]	T. Hofmann, “Latent semantic models for collaborative filtering,” ACM Trans. Inf. Syst., vol. 22, no. 1, pp. 89–115, Jan. 2004. doi: 10.1145/963770.963774
[18]	S. Gauch, M. Speretta, A. Chandramouli, and A. Micarelli, “User profiles for personalized information access,” in The Adaptive Web, P. Brusilovsky, A. Kobsa, and W. Nejdl, Eds. Berlin, Germany: Springer, 2007, pp. 54–89.
[19]	R. Wittek, T. A. B. Snijders, and V. Nee, The Handbook of Rational Choice Social Research. Palo Alto, USA: Stanford University Press, 2013, pp. 252–279.
[20]	J. Golbeck and J. Hendler, “Inferring binary trust relationships in web-based social networks,” ACM Trans. Internet Technol., vol. 6, no. 4, pp. 497–529, Nov. 2006. doi: 10.1145/1183463.1183470
[21]	W. J. Jiang, J. Wu, and G. J. Wang, “On selecting recommenders for trust evaluation in online social networks,” ACM Trans. Internet Technol., vol. 15, no. 4, pp. 14, Nov. 2015.
[22]	R. Kali, “Social embeddedness and economic governance: A small world approach,” Department of Economics, University of Arkansas, USA, 2003. [Online]. Available: https://ssrn.com/abstract=462560
[23]	M. Granovetter, “Economic action and social structure: The problem of embeddedness,” Am. J. Sociol., vol. 91, no. 3, pp. 481–510, Nov. 1985. doi: 10.1086/228311
[24]	A. A. Rad and M. Benyoucef, “Similarity and ties in social networks: a study of the youtube social network,” J. Inf. Syst. Appl. Res., vol. 7, no. 4, pp. 14–24, Nov. 2014.
[25]	V. Agarwal and K. Bharadwaj, “A collaborative filtering framework for friends recommendation in social networks based on interaction intensity and adaptive user similarity,” Soc. Network Anal. Min., vol. 3, no. 3, pp. 359–379, Sept. 2013. doi: 10.1007/s13278-012-0083-7
[26]	R. Lande, “Statistics and partitioning of species diversity, and similarity among multiple communities,” Oikos, vol. 76, no. 1, pp. 5–13, May 1996. doi: 10.2307/3545743
[27]	X. J. Wang, Z. L. Ning, M. C. Zhou, X. P. Hu, L. Wang, Y. Zhang, F. R. Yu, and B. Hu, “Privacy-preserving content dissemination for vehicular social networks: Challenges and solutions,” IEEE Commun. Surv. Tutor., vol. 21, no. 2, pp. 1314–1345, 2019. doi: 10.1109/COMST.2018.2882064
[28]	Y. S. Lv, Y. Y. Chen, X. Q. Zhang, Y. J. Duan, and N. L. Li, “Social media based transportation research: the state of the work and the networking,” IEEE/CAA J. Autom. Sinica, vol. 4, no. 1, pp. 19–26, Jan. 2017. doi: 10.1109/JAS.2017.7510316
[29]	P. Y. Zhang, M. C. Zhou, and Y. Kong, “A double-blind anonymous evaluation-based trust model in cloud computing environments,” IEEE Trans. Syst.,Man,Cybernet.:Syst., pp. 1–12, Apr. 2019. doi: 10.1109/TSMC.2019.2906310
[30]	P. Y. Zhang, Y. Kong, and M. C. Zhou, “A domain partition-based trust model for unreliable clouds,” IEEE Trans. Inf. Forens. Secur., vol. 13, no. 9, pp. 2167–2178, Sept. 2018. doi: 10.1109/TIFS.2018.2812166
[31]	Y. Wang and J. Vassileva, “Trust-based community formation in peer-to-peer file sharing networks,” in Proc. IEEE/WIC/ACM Int. Conf. Web Intelligence, Beijing, China, 2004, pp. 341–348.
[32]	P. De Meo, A. Nocera, G. Quattrone, D. Rosaci, and D. Ursino, “Finding reliable users and social networks in a social internetworking system,” in Proc. 2009 Int. Database Engineering & Applications Symp, Calabria, Italy, 2009, pp. 173–181.
[33]	P. De Meo, A. Nocera, D. Rosaci, and D. Ursino, “Recommendation of reliable users, social networks and high-quality resources in a social internetworking system,” AI Commun., vol. 24, no. 1, pp. 31–50, Jan. 2011. doi: 10.3233/AIC-2010-0484
[34]	H. B. Zhu, “Predict team performance with E-CARGO,” in Proc. 2018 IEEE Int. Conf. Systems, Man, and Cybernetics, Miyazaki, Japan, 2018, pp. 3559–3564.
[35]	H. B. Zhu, M. C. Zhou, and R. Alkins, “Group role assignment via a Kuhn-munkres algorithm-based solution,” IEEE Trans. Syst.,Man,Cybernet. Part A:Syst. Humans, vol. 42, no. 3, pp. 739–750, May 2012. doi: 10.1109/TSMCA.2011.2170414
[36]	S. J. Brams and P. C. Fishburn, “Voting procedures,” in Handbook of Social Choice and Welfare, K. J. Arrow, A. K. Sen, and K. Suzumura, Eds. Amsterdam, USA: Elsevier, 2002, pp. 173–236.
[37]	N. R. Council, Public Participation in Environmental Assessment and Decision Making. Washington, USA: National Academies Press, 2008.
[38]	T. C. Beierle and J. Cayford, Democracy in Practice: Public Participation in Environmental Decisions. Washington, USA: Resources for the Future Press, 2002.
[39]	L. Xia, “Computational voting theory: game-theoretic and combinatorial aspects,” Ph.D. dissertation, Duke Univ., North Carolina, USA, 2011.
[40]	Y. Chevaleyre, U. Endriss, J. Lang, and N. Maudet, “A short introduction to computational social choice,” in Proc. 33rd Conf. Current Trends in Theory and Practice of Computer Science, Harrachov, Czech Republic, 2017, pp. 51–69.
[41]	V. Conitzer and T. Sandholm, “Universal voting protocol tweaks to make manipulation hard,” in Proc. 18th Int. Joint Conf. Artificial Intelligence, Acapulco, Mexico, 2003.
[42]	K. J. Arrow, Social Choice and Individual Values. 3rd ed. New Haven, USA: Yale University Press, 2012.
[43]	M. A. Satterthwaite, “Strategy-proofness and arrow’s conditions: Existence and correspondence theorems for voting procedures and social welfare functions,” J. Econom. Theory, vol. 10, no. 2, pp. 187–217, Apr. 1975. doi: 10.1016/0022-0531(75)90050-2
[44]	V. Conitzer and T. Walsh, “Barriers to manipulation in voting,” in Handbook of Computational Social Choice, F. Brandt, V. Conitzer, U. Endriss, J. Lang, A. D. Procaccia, and H. Moulin, Eds. Cambridge, USA: Cambridge University Press, 2016.
[45]	J. Pitt, L. Kamara, M. Sergot, and A. Artikis, “Formalization of a voting protocol for virtual organizations,” in Proc. 4th Int. Joint Conf. Autonomous Agents and Multiagent Systems, Utrecht, The Netherlands, 2005, pp. 373–380.
[46]	D. A. Gritzalis, “Principles and requirements for a secure e-voting system,” Comput. Secur., vol. 21, no. 6, pp. 539–556, Oct. 2002. doi: 10.1016/S0167-4048(02)01014-3
[47]	M. Bishop and D. Wagner, “Risks of e-voting,” Commun. ACM, vol. 50, no. 11, pp. 120, Nov. 2007. doi: 10.1145/1297797.1297827
[48]	B. Misztal, Trust in Modern Societies: The Search for the Bases of Social Order. New York, USA: John Wiley & Sons, 2013.
[49]	S. Gächter, B. Herrmann, and C. Thöni, “Trust, voluntary cooperation, and socio-economic background: Survey and experimental evidence,” J. Econom. Behav. Organ., vol. 55, no. 4, pp. 505–531, Dec. 2004. doi: 10.1016/j.jebo.2003.11.006
[50]	D. Artz and Y. Gil, “A survey of trust in computer science and the semantic web,” J. Web Semant., vol. 5, no. 2, pp. 58–71, Jun. 2007. doi: 10.1016/j.websem.2007.03.002
[51]	S. Singh and S. Bawa, “A privacy, trust and policy based authorization framework for services in distributed environments,” Int. J. Comput. Sci., vol. 2, no. 2, pp. 85–92, 2007.
[52]	W. Sherchan, S. Nepal, and C. Paris, “A survey of trust in social networks,” ACM Comput. Surv., vol. 45, no. 4, pp. 47, Aug. 2013.
[53]	P. Dumouchel, “Trust as an action,” Eur. J. Sociol./Arch. Eur. Sociol., vol. 46, no. 3, pp. 417–428, Dec. 2005. doi: 10.1017/S0003975605000160
[54]	M. Levi, “A state of trust,” in Trust and Governance, V. Braithwaite and M. Levi, Eds. New York, USA: Russell Sage Foundation, 1998, pp. 77–101.
[55]	T. Jiang and J. S. Baras, “Trust evaluation in anarchy: A case study on autonomous networks,” in Proc. 25th IEEE Int. Conf. Computer Communications, Barcelona, Spain, 2006.
[56]	G. Theodorakopoulos and J. S. Baras, “On trust models and trust evaluation metrics for Ad Hoc networks,” IEEE J. Sel. Areas Commun., vol. 24, no. 2, pp. 318–328, Feb. 2006. doi: 10.1109/JSAC.2005.861390
[57]	M. Mohri, “Semiring frameworks and algorithms for shortest-distance problems,” J. Autom.,Lang. Comb., vol. 7, no. 3, pp. 321–350, Jan. 2002.
[58]	S. E. Williams, L. P. Shoo, J. L. Isaac, A. A. Hoffmann, and G. Langham, “Towards an integrated framework for assessing the vulnerability of species to climate change,” PLoS Biol., vol. 6, no. 12, pp. e325, Dec. 2008. doi: 10.1371/journal.pbio.0060325

Supplements(0)

Cited By

Proportional views

Proportional views

通讯作者: 陈斌, bchen63@163.com

1.
沈阳化工大学材料科学与工程学院沈阳 110142

Figures(8) / Tables(1)

Get Citation

PDF

XML

Article Metrics

Article views (1383) PDF downloads(34)

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

Abstract

References

Proportional views

Catalog

通讯作者: 陈斌, bchen63@163.com

Article Metrics

Highlights

Export File

Citation

Format

Content