Social Network Analysis and Mining

, Volume 3, Issue 1, pp 93–106 | Cite as

Enabling cross-site interactions in social networks

Review Article
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Abstract

Online social networks is one of the major technological phenomena on the Web 2.0. Hundreds of millions of people are posting articles, photos, and videos on their profiles and interacting with other people, but the sharing and interaction are limited within a same social network site. Although users can share some contents in a social network site with people outside of the social network site using a secret address of content, appropriate access control mechanisms are still not supported. To overcome this limitation, we propose a cross-site interaction framework x-mngr, allowing users to interact with users in other social network sites, with a cross-site access control policy, which enables users to specify policies that allow/deny access to their shared contents across social network sites. We also propose a partial mapping approach based on a supervised learning mechanism to map user’s identities across social network sites. We implemented our proposed framework through a photo album sharing application that shares user’s photos between Facebook and MySpace based on the cross-site access control policy that is defined by the content owner. Furthermore, we provide mechanisms to enable users to fuse user-mapping decisions that are provided by their friends or others in the social network. We implemented our framework and through extensive experimentation we prove the accuracy and precision of our proposed mechanisms.

Keywords

Social networks Profile mapping Supervised learning 
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Notes

Acknowledgments

This work was partially funded by the National Science Foundation (NSF-CNS-0831360 and NSF-CNS-1117411).

References

  1. Barbosa L, Freire J (2007) Combining classifiers to identify online databases. In: WWW ’07: Proceedings of the 16th international conference on World Wide Web. ACM, New York, pp 431–440Google Scholar
  2. Barreno M, Bartlett PL, Chi FJ, Joseph AD, Nelson B, Rubinstein BI, Saini U, Tygar JD (2008) Open problems in the security of learning. In: AISec ’08: Proceedings of the 1st ACM workshop on Workshop on AISec. ACM, New York, pp 19–26 Google Scholar
  3. Borgatti SP, Everett MG (2006) A graph-theoretic perspective on centrality. Soc Netw 28(4):466–484CrossRefGoogle Scholar
  4. Boyd D (2008) Taken out of context: American teen sociality in networked publics. Phd dissertation, University of California-Berkeley, School of Information Google Scholar
  5. Branckaute F (2010) Twitter’s meteoric rise compared to facebook [infographic]. The Blog Herald Google Scholar
  6. Brandes U, Erlebach T (2005) Network analysis: methodological foundations, 1st edn. Springer, Berlin, HeidelbergGoogle Scholar
  7. Bratko A, Filipič B, Cormack GV, Lynam TR, Zupan B (2006) Spam filtering using statistical data compression models. J Mach Learn Res 7:2673–2698MathSciNetMATHGoogle Scholar
  8. Carminati B, Ferrari E, Perego A (2006) Rule-based access control for social networks. In: On the move to meaningful internet systems 2006: OTM 2006 Workshops, vol 4278, Lecture Notes in Computer Science. Springer, Berlin, Heidelberg, pp 1734–1744Google Scholar
  9. Fang L, LeFevre K (2010) Privacy wizards for social networking sites. In: Proceedings of the 19th international conference on World wide web, WWW ’10. ACM, New York, pp 351–360Google Scholar
  10. Fazeen M, Dantu R, Guturu P (2011) Identification of leaders, lurkers, associates and spammers in a social network: context-dependent and context-independent approaches. Soc Netw Anal Min 1:241–254CrossRefGoogle Scholar
  11. Fumera G, Pillai I, Roli F (2006) Spam filtering based on the analysis of text information embedded into images. J Mach Learn Res 7: 2699–2720Google Scholar
  12. Gilbert F, Simonetto P, Zaidi F, Jourdan F, Bourqui R (2011) Communities and hierarchical structures in dynamic social networks: analysis and visualization. Soc Netw Anal Min 1:83–95CrossRefGoogle Scholar
  13. Gollu KK, Saroiu S, Wolman A (2007) A social networking-based access control scheme for personal content. In: Proceedings of 21st ACM symposium on operating systems principles (SOSP ’07). Work in progressGoogle Scholar
  14. Gong L, Qian X (1994) The complexity and composability of secure interoperation. In: SP ’94: Proceedings of IEEE symposium on security and privacy. IEEE Computer Society, pp 190–200Google Scholar
  15. Gong L, Qian X (1996) Computational issues in secure interoperation. IEEE Transact Softw Eng 22(1):43–52CrossRefGoogle Scholar
  16. Google Inc. (2009) Google Maps API Services. http://code.google.com/apis/maps/
  17. Gummelt M (2010) Publishing to twitter from facebook pages, http://blog.facebook.com/blog.php?post=123006872130
  18. Kittler J, Hatef M, Duin RP, Matas J (1998) On combining classifiers. IEEE Transact Pattern Anal Mach Intelligence 20(3):226–239CrossRefGoogle Scholar
  19. Kleinberg JM (1999) Authoritative sources in a hyperlinked environment. J ACM 46(5):604–632MathSciNetMATHCrossRefGoogle Scholar
  20. Levenshtein VI (1966) Binary codes capable of correcting deletions, insertions, and reversals. Technical Report 8, Soviet Physics Doklady Google Scholar
  21. Liu B (2007) Web Data Mining: Exploring Hyperlinks, Contents, and Usage Data (Data-Centric Systems and Applications), 1st edn. Springer Google Scholar
  22. Maes S, Tuyls K, Vanschoenwinkel B, Manderick B (1993) Credit card fraud detection using bayesian and neural networks. In: Maciunas RJ (ed) Interactive image-guided neurosurgery. American Association Neurological Surgeons, pp 261–270Google Scholar
  23. Newman MEJ (2001) Scientific collaboration networks. II. Shortest paths, weighted networks, and centrality. Phys Rev E 64(1):016132 Google Scholar
  24. Newman MEJ (2003) The structure and function of complex networks. SIAM Rev 45(2):167–256MathSciNetMATHCrossRefGoogle Scholar
  25. Ni Q, Lobo J, Calo S, Rohatgi P, Bertino E (2009) Automating role-based provisioning by learning from examples. In: The Proceedings of the 14th ACM symposium on Access control models and technologies (SACMAT 2009)Google Scholar
  26. Patriquin A (2007) Connecting the social graph: member overlap at opensocial and facebook, http://blog.compete.com/2007/11/12/connecting-the-social-graph-member-overlap-at-opensocial-and-facebook/
  27. Perols J, Chari K, Agrawal M (2009) Information market-based decision fusion. Manage Sci 55(5):827–842CrossRefGoogle Scholar
  28. Saravanan M, Prasad G, Karishma S, Suganthi D (2011) Analyzing and labeling telecom communities using structural properties. Soc Netw Anal Min 1:271–286CrossRefGoogle Scholar
  29. Schwartz B (2010) How to connect twitter to facebook status updates. http://www.ehow.com/how_4668396_connect-twitter-facebook-status-updates.html
  30. Scott J (2011) Social network analysis: developments, advances, and prospects. Soc Netw Anal Min 1:21–26CrossRefGoogle Scholar
  31. Shehab M, Bertino E, Ghafoor A (2005) Secure collaboration in mediator-free environments. In: Proceedings of the 12th ACM conference on computer and communications security, CCS ’05. ACM Press, New York, pp 58–67Google Scholar
  32. Shehab M, Cheek G, Touati H, Squicciarini AC, Cheng PC (2010) Learning based access control in online social networks. In: Proceedings of the 19th international conference on World wide web, WWW ’10. ACM, New York Google Scholar
  33. Short JF, Hughes LA (2006) Studying youth gangs. AltaMira Press, New YorkGoogle Scholar
  34. Sinclair C, Pierce L, Matzner S (1999) An application of machine learning to network intrusion detection. Computer Secur Appl Conf Ann 0:371Google Scholar
  35. The JUNG Framework Development Team: Java Universal Network/Graph Framework (2009) http://jung.sourceforge.net/
  36. The University of Waikato: WEKA Machine Learning Project (2009) http://www.cs.waikato.ac.nz/ml/index.html
  37. Wang G, Chen H, Atabakhsh H (2004) Automatically detecting deceptive criminal identities. Commun ACM 47(3):70–76CrossRefGoogle Scholar
  38. Wang GA, Chen H, Xu JJ, Atabakhsh H (2006) Automatically detecting criminal identity deception: an adaptive detection algorithm. IEEE Trans Syst Man Cybern Part A 36(5):988–999CrossRefGoogle Scholar
  39. Wasserman S, Faust K (1994) Social network analysis: methods and applications, illustrated edn. Cambridge University Press, CambridgeGoogle Scholar
  40. Witten IH, Frank E (2005) Data Mining: practical machine learning tools and techniques, 2nd edn. Morgan Kaufmann Series in Data Management Systems. Morgan Kaufmann, San Francisco. ISBN: 0120884070Google Scholar
  41. Xu J, Wang G.A, Li J, Chau M (2007) Complex problem solving: identity matching based on social contextual information. J Assoc Inform Syst 8(10):525–545 (Article 2)Google Scholar

Copyright information

© Springer-Verlag 2012

Authors and Affiliations

  1. 1.Department of Software and Information Systems, College of Computing and InformaticsUniversity of North CarolinaCharlotteUSA

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