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Cognitive social network analysis for supporting the reliable decision-making process

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Abstract

With the advancement of the Internet and web technologies, social networks have gained attention as a new paradigm for user-centered information systems. As the amount of accessible information increases, the need for personalized information increases as well. Under such circumstances, social networks that are based on trust between users are increasingly utilized to provide efficient and reliable information management. This paper proposes the cognitive social network analysis that analyzes relationships between users with typical properties. The proposed approach analyzes users’ habitual activities and creates a local social network. The framework then integrates the local networks via the friend of a friend, thereby creating a global social network. User relationships in the global network are reinforced to maximize information sharing. To evaluate the performance of the information shared in the proposed autonomic cognitive social network framework, the accuracy of the information associated with social network was measured using the ROC Curve. In future, we should analyze the social influence factors from relationship between community and users.

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References

  1. Yager RR (2008) Granular computing for intelligent social network modeling and cooperative decisions. In: Presented at the International IEEE Conference Intelligent Systems

  2. Adar E, Ré C (2007) Managing uncertainty in social networks. IEEE Data Eng Bull 30(2):15–22

    Google Scholar 

  3. Borgatti SP, Mehra A, Brass DJ, Labianca G (2009) Network analysis in the social sciences. Science 323(5916):892–895

    Article  Google Scholar 

  4. De Rijke M, Weerkamp W (2008) ECIR 2008 tutorials: search and discovery in user-generated text content. In: Proceedings of the IR Research, 30th European Conference on Advances in Information Retrieval, pp 714–715

  5. Langville AN, Meyer CD (2011) Google’s PageRank and beyond: the science of search engine rankings. Princeton University Press, Princeton

    MATH  Google Scholar 

  6. Staab S, Domingos P, Mike P, Golbeck J, Ding L, Finin T, Josh A, Nowak A, Vallacher RR (2005) Social networks applied. IEEE Intell Syst 20(1):80–93

    Article  Google Scholar 

  7. Breslin J, Decker S (2007) The future of social networks on the internet: the need for semantics. IEEE Internet Comput 11(6):86–90

    Article  Google Scholar 

  8. Cheung CM, Lee MK (2009) Understanding the sustainability of a virtual community: model development and empirical test. J Inf Sci 35(3):279–298

  9. Xu Z, Tresp V, Rettinger A, Kersting K (2010) Social network mining with nonparametric relational models. In: Advances in social network mining and analysis, pp 77–96

  10. Grandison T, Sloman M (2000) A survey of trust in internet applications. IEEE Commun Surv Tutor 3(4):2–16

    Article  Google Scholar 

  11. Golbeck J (2009) Trust and nuanced profile similarity in online social networks. ACM Trans Web (TWEB) 3(4):12

    Google Scholar 

  12. Borcea-Pfitzmann K, Hansen M, Liesebach K, Pfitzmann A, Steinbrecher S (2006) What user-controlled identity management should learn from communities. Inf Secur Tech Rep 11(3):119–128

    Article  Google Scholar 

  13. Backstrom L, Huttenlocher D, Kleinberg J, Lan X (2006) Group formation in large social networks: membership, growth, and evolution. In: Proceedings of the 12th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp 44–54

  14. Lampinen A, Tamminen S, Oulasvirta A (2009) All my people right here, right now: management of group co-presence on a social networking site. In: Proceedings of the ACM 2009 International Conference on Supporting Group Work, pp 281–290

  15. Toahchoodee M, Abdunabi R, Ray I, Ray I (2009) A trust-based access control model for pervasive computing applications. In: Data and applications security XXIII, pp 307–314

  16. Resnick P, Zeckhauser R (2002) Trust among strangers in Internet transactions: empirical analysis of eBay’s reputation system. Adv Appl Microecon 11:127–157

    Article  Google Scholar 

  17. O’Donovan J, Smyth B (2005) Trust in recommender systems. In: Proceedings of the 10th International Conference on Intelligent User Interfaces, pp 167–174

  18. Bae J, Kim S (2009) A global social graph as a hybrid hypergraph. In: NCM’09. Fifth International Joint Conference, pp 1025–1031

  19. Bourqui R, Gilbert F, Simonetto P, Zaidi F, Sharan U, Jourdan F (2009) Detecting structural changes and command hierarchies in dynamic social networks. In: International Conference on Advances in Social Network Analysis and Mining, pp 83–88

  20. Liben-Nowell D, Kleinberg J (2007) The link-prediction problem for social networks. J Am Soc Inf Sci Technol 58(7):1019–1031

    Article  Google Scholar 

  21. Yoo S, Yang Y, Lin F, Moon IC (2009) Mining social networks for personalized email prioritization. In: Proceedings of the 15th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp 967–976

  22. Debnath S, Ganguly N, Mitra P (2008) Feature weighting in content based recommendation system using social network analysis. In: Proceedings of the 17th International Conference on World Wide Web, pp 1041–1042

  23. Kim M, Seo J, Noh S, Han S (2012) Identity management-based social trust model for mediating information sharing and privacy enhancement. Secur Commun Netw 5(8):887–897

    Article  Google Scholar 

  24. Tanis M, Postmes T (2005) A social identity approach to trust: Interpersonal perception, group membership and trusting behaviour. Eur J Soc Psychol 35(3):413–424

    Article  Google Scholar 

  25. Arthur D, Motwani R, Sharma A, Xu Y (2009) Pricing strategies for viral marketing on social networks. In: Internet and network economics, pp 101–112

  26. Kim M, Park SO (2013) Group affinity based social trust model for an intelligent movie recommender system. Multimed Tools Appl 64(2):505–516

    Article  Google Scholar 

  27. Ankolekar A, Szabo G, Luon Y, Huberman BA, Wilkinson D, Wu F (2009) Friendlee: a mobile application for your social life. In: Proceedings of the 11th International Conference on Human-Computer Interaction with Mobile Devices and Services

  28. Mislove A, Viswanath B, Gummadi KP, Druschel P (2010) You are who you know: inferring user profiles in online social networks. In: Proceedings of the Third ACM International Conference on Web Search and Data Mining, pp 251–260

  29. Fraley C, Raftery AE (1998) How many clusters? Which clustering method? Answers via model-based cluster analysis. Comput J 41(8):578–588

    Article  MATH  Google Scholar 

  30. Li Y, Chung SM, Holt JD (2008) Text document clustering based on frequent word meaning sequences. Data Knowl Eng 64(1):381–404

    Article  Google Scholar 

  31. Nigam K, McCallum AK, Thrun S, Mitchell T (2000) Text classification from labeled and unlabeled documents using EM. Mach Learn 39(2–3):103–134

    Article  MATH  Google Scholar 

  32. Newman ME (2001) Clustering and preferential attachment in growing networks. Phys Rev E 64(2):025102

    Article  Google Scholar 

  33. Jain AK, Murty MN, Flynn PJ (1999) Data clustering: a review. ACM Comput Surv (CSUR) 31(3):264–323

    Article  Google Scholar 

  34. Seo Y (2010) Advanced access control mechanism based on trust and risk for collaboration networks and online social networks. Ph. D thesis, Chung-Ang University

  35. MovieLens Data Set. http://www.cs.umn.edu/Research/GroupLens

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Acknowledgments

This work was supported by Institute for Information & communications Technology Promotion (IITP) Grant funded by the Korea Government(MSIP) (No. R0126-15-1007, Curation commerce based global open market system development for personal happiness enhancement).

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Correspondence to Sangyong Han.

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Kim, M., Han, S. Cognitive social network analysis for supporting the reliable decision-making process. J Supercomput 74, 3654–3665 (2018). https://doi.org/10.1007/s11227-016-1858-9

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  • DOI: https://doi.org/10.1007/s11227-016-1858-9

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