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A Model to Support Multi-Social-Network Applications

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On the Move to Meaningful Internet Systems: OTM 2014 Conferences (OTM 2014)

Part of the book series: Lecture Notes in Computer Science ((LNISA,volume 8841))

Abstract

It is not uncommon that people create multiple profiles in different social networks, spreading out over them personal information. This leads to a multi-social-network scenario where different social networks cannot be viewed as monads, but are strongly correlated to each other. Building a suitable middleware on top of social networks to support internetworking applications is an important challenge, as the global view of the social network world provides very powerful knowledge and opportunities. In this paper, we do a first important step towards this goal, by defining and implementing a model aimed at generalizing concepts, actions and relationships of existing social networks.

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References

  1. Videotrine. The most viewed videos on Youtube in the World of all time (2014), http://en.videotrine.com/all/youtube/all-time

  2. Brickley, D., Miller, L.: FOAF Vocabulary Specification 0.91. Technical report, Tech. rep. ILRT Bristol (2000), http://xmlns.com/foaf/spec/20071002.html

  3. Buccafurri, F., Foti, V., Lax, G., Nocera, A., Ursino, D.: Bridge Analysis in a Social Internetworking Scenario. Information Sciences 224, 1–18 (2013)

    Article  MathSciNet  Google Scholar 

  4. Buccafurri, F., Lax, G., Nicolazzo, S., Nocera, A., Ursino, D.: Driving Global Team Formation in Social Networks to Obtain Diversity. In: Casteleyn, S., Rossi, G., Winckler, M. (eds.) ICWE 2014. LNCS, vol. 8541, pp. 410–419. Springer, Heidelberg (2014)

    Chapter  Google Scholar 

  5. Buccafurri, F., Lax, G., Nocera, A., Ursino, D.: Crawling social internetworking systems. In: Proceedings of the 2012 International Conference on Advances in Social Networks Analysis and Mining (ASONAM 2012), pp. 506–510. IEEE Computer Society (2012)

    Google Scholar 

  6. Buccafurri, F., Lax, G., Nocera, A., Ursino, D.: Discovering Links among Social Networks. In: Flach, P.A., De Bie, T., Cristianini, N. (eds.) ECML PKDD 2012, Part II. LNCS, vol. 7524, pp. 467–482. Springer, Heidelberg (2012)

    Chapter  Google Scholar 

  7. Buccafurri, F., Lax, G., Nocera, A., Ursino, D.: Moving from social networks to social internetworking scenarios: The crawling perspective. Information Sciences 256, 126–137 (2014)

    Article  MathSciNet  Google Scholar 

  8. Buccafurri, F., Lax, G., Nocera, A., Ursino, D.: A system for extracting structural information from social network accounts. Software: Practice and Experience (2014), doi:10.1002/spe.2280

    Google Scholar 

  9. Caldarelli, G.: Scale-Free Networks: Complex Webs in Nature and Technology. Number 9780199211517 in OUP Catalogue. Oxford University Press (2007)

    Google Scholar 

  10. Carmagnola, F., Cena, F.: User identification for cross-system personalisation. Information Sciences 179(1-2), 16–32 (2009)

    Article  Google Scholar 

  11. Erdös, P., Rényi, A.: On Random Graphs, I. Publicationes Mathematicae 6, 290–297 (1959)

    MATH  Google Scholar 

  12. Ghoshal, G., Zlatić, V., Caldarelli, G., Newman, M.E.J.: Random hypergraphs and their applications. Physical Review E 79(6), 066118 (2009)

    Article  Google Scholar 

  13. Greve, A., Salaff, J.W.: Social networks and entrepreneurship. Entrepreneurship Theory and Practice 28(1), 1–22 (2003)

    Article  Google Scholar 

  14. Iofciu, T., Fankhauser, P., Abel, F., Bischoff, K.: Identifying users across social tagging systems. In: Proc. of the International Conference on Weblogs and Social Media (ICWSM 2011), Barcelona, Catalonia, Spain. The AAAI Press (2011)

    Google Scholar 

  15. Iturrioz, J., Diaz, O., Arellano, C.: Towards federated web2. 0 sites: The tagmas approach. In: Tagging and Metadata for Social Information Organization Workshop, WWW 2007 (2007)

    Google Scholar 

  16. Karypis, G., Aggarwal, R., Kumar, V., Shekhar, S.: Multilevel hypergraph partitioning: applications in VLSI domain. IEEE Transactions on Very Large Scale Integration (VLSI) Systems 7(1), 69–79 (1999)

    Article  Google Scholar 

  17. Kim, M., Leskovec, J.: Modeling social networks with node attributes using the multiplicative attribute graph model. arXiv preprint arXiv:1106.5053 (2011)

    Google Scholar 

  18. Leenders, R.T.: Modeling social influence through network autocorrelation: constructing the weight matrix. Social Networks 24(1), 21–47 (2002)

    Article  Google Scholar 

  19. Leskovec, J., Chakrabarti, D., Kleinberg, J., Faloutsos, C., Ghahramani, Z.: Kronecker graphs: An approach to modeling networks. The Journal of Machine Learning Research 11, 985–1042 (2010)

    MATH  MathSciNet  Google Scholar 

  20. Lovász, L.: Random walks on graphs: A survey. Combinatorics, Paul Erdos is Eighty 2(1), 1–46 (1993)

    Google Scholar 

  21. Mika, P.: Ontologies are us: A unified model of social networks and semantics. In: Gil, Y., Motta, E., Benjamins, V.R., Musen, M.A. (eds.) ISWC 2005. LNCS, vol. 3729, pp. 522–536. Springer, Heidelberg (2005)

    Chapter  Google Scholar 

  22. Newman, M.E.J., Watts, D.J., Strogatz, S.H.: Random graph models of social networks. Proceedings of the National Academy of Sciences 99(suppl. 1), 2566–2572 (2002)

    Article  MATH  Google Scholar 

  23. Noor, S., Martinez, K.: Using social data as context for making recommendations: an ontology based approach. In: Proceedings of the 1st Workshop on Context, Information and Ontologies, p. 7. ACM (2009)

    Google Scholar 

  24. Romm, C., Pliskin, N., Clarke, R.: Virtual communities and society: toward an integrative three phase model. International Journal of Information Management 17(4), 261–270 (1997)

    Article  Google Scholar 

  25. Specia, L., Motta, E.: Integrating folksonomies with the semantic web. In: Franconi, E., Kifer, M., May, W. (eds.) ESWC 2007. LNCS, vol. 4519, pp. 624–639. Springer, Heidelberg (2007)

    Google Scholar 

  26. Stewart, A., Diaz-Aviles, E., Nejdl, W., Marinho, L.B., Nanopoulos, A., Schmidt-Thieme, L.: Cross-tagging for personalized open social networking. In: Proceedings of the 20th ACM Conference on Hypertext and Hypermedia, pp. 271–278. ACM (2009)

    Google Scholar 

  27. Stutzback, D., Rejaie, R., Duffield, N., Sen, S., Willinger, W.: On unbiased sampling for unstructured peer-to-peer networks. In: Proc. of the International Conference on Internet Measurements, Rio De Janeiro, Brasil, pp. 27–40. ACM (2006)

    Google Scholar 

  28. Wang, A.H.: Don’t follow me: Spam detection in twitter. In: Proceedings of the 2010 International Conference on Security and Cryptography (SECRYPT), pp. 1–10. IEEE (2010)

    Google Scholar 

  29. Wang, X., Wei, F., Liu, X., Zhou, M., Zhang, M.: Topic sentiment analysis in twitter: a graph-based hashtag sentiment classification approach. In: Proceedings of the 20th ACM International Conference on Information and Knowledge Management, pp. 1031–1040. ACM (2011)

    Google Scholar 

  30. Wilken, P.H.: Entrepreneurship: A comparative and historical study. Ablex, Norwood (1979)

    Google Scholar 

  31. Ye, S., Lang, J., Wu, F.: Crawling online social graphs. In: Proc. of the International Asia-Pacific Web Conference (APWeb 2010), Busan, Korea, pp. 236–242. IEEE (2010)

    Google Scholar 

  32. Zafarani, R., Liu, H.: Connecting corresponding identities across communities. In: Proc. of the International Conference on Weblogs and Social Media (ICWSM 2009), San Jose, CA, USA. The AAAI Press (2009)

    Google Scholar 

  33. Zhang, Z., Liu, C.: A hypergraph model of social tagging networks. Journal of Statistical Mechanics: Theory and Experiment 2010(10), P10005 (2010)

    Google Scholar 

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Buccafurri, F., Lax, G., Nicolazzo, S., Nocera, A. (2014). A Model to Support Multi-Social-Network Applications. In: Meersman, R., et al. On the Move to Meaningful Internet Systems: OTM 2014 Conferences. OTM 2014. Lecture Notes in Computer Science, vol 8841. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-662-45563-0_39

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  • DOI: https://doi.org/10.1007/978-3-662-45563-0_39

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-662-45562-3

  • Online ISBN: 978-3-662-45563-0

  • eBook Packages: Computer ScienceComputer Science (R0)

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