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Social Network Analysis and Mining

, Volume 3, Issue 3, pp 667–683 | Cite as

A community-based algorithm for deriving users’ profiles from egocentrics networks: experiment on Facebook and DBLP

  • Dieudonné TchuenteEmail author
  • Marie-Francoise Canut
  • Nadine Jessel
  • André Peninou
  • Florence Sèdes
Original Article

Abstract

Nowadays, social networks are more and more widely used as a solution for enriching users’ profiles in systems such as recommender systems or personalized systems. For an unknown user’s interest, the user’s social network can be a meaningful data source for deriving that interest. However, in the literature very few techniques are designed to meet this solution. Existing techniques usually focus on people individually selected in the user’s social network and strongly depend on each author’s objective. To improve these techniques, we propose using a community-based algorithm that is applied to a part of the user’s social network (egocentric network) and that derives a user social profile that can be reused for any purpose (e.g., personalization, recommendation). We compute weighted user’s interests from these communities by considering their semantics (interests related to communities) and their structural measures (e.g., centrality measures) in the egocentric network graph. A first experiment conducted in Facebook demonstrates the usefulness of this technique compared to individual-based techniques and the influence of structural measures (related to communities) on the quality of derived profiles. A second experiment on DBLP and the author’s social network Mendeley confirms the results obtained on Facebook and shows the influence of the density of egocentrics network on the quality of results.

Keywords

User profile Social network Egocentric network Social profiling Facebook DBLP 

References

  1. Aiello LM, Barrat A, Cattuto C, Ruffo G, Schifanella R (2010) Link Creation and Profile Alignment in the aNobii Social Network. SocialCom/PASSAT pp 249–256Google Scholar
  2. Bender M, Crecelius T, Kacimi M, Michel S, Neumann T, Parreira JX, Schenkel R, Weikum G (2008) “Exploiting social relations for query expansion and result ranking” In: IEEE 24th International Conference on Data Engineering Workshop, ICDEW 2008. vol no 7–12 pp 501–506Google Scholar
  3. Bhattacharyya P, Garg A, Felix WuS (2011) Analysis of user keyword similarity in online social networks. Soc Netw Anal Min 1(3):143–158 SpringerCrossRefGoogle Scholar
  4. Bonhard P, Sasse MA (2006) “Knowing me, Knowing you – using profiles and social networking to improve recommender systems”, BT Technology Journal, vol 24 No 3Google Scholar
  5. Cabanac G (2011) Accuracy of inter-researcher similarity measures based on topical and social clues. Scientometrics 87(3):597–620CrossRefGoogle Scholar
  6. Carmel D, Zwerdling N, Guy I, Ofek-Koifman S, Har’el N, Ronen I, Uziel E, Yogev S, Chernov S (2009) “Personalized social search based on the user’s social network”, In: 18th ACM conference on Information and knowledge management (CIKM ‘09). ACM, New York, NY, USA, pp 1227–1236Google Scholar
  7. Cazabet R, Amblard F, Hanachi C (2010) “Detection of overlapping communities in dynamical social networks” In: IEEE second international conference on social computing (social com), 2010 vol no. 20–22, pp 309–314Google Scholar
  8. Cazabet R, Maud L, Amblard F (2012) Automatic community detection in online social networks: useful? efficient? asking the users. In: The 4th international workshop on web intelligence and communities, Lyon, pp 6.1–6.8Google Scholar
  9. Cazabet R, Takeda H, Hamasaki M, Amblard F (2012b) Using dynamic community detection to identify trends in user-generated content. Soc Netw Anal Min 2(4):361–371 SpringerCrossRefGoogle Scholar
  10. Esllimani I, Brun A, Boyer A (2011) Densifying a behavioral recommender systems by social networks link prediction methods. Soc Netw Anal Min 1(3):159–172 SpringerCrossRefGoogle Scholar
  11. Everett MG, Borgatti SP (1999) The centrality of groups and classes. J Math Sociol 23(3):181–201MathSciNetzbMATHCrossRefGoogle Scholar
  12. Fox EA, Shaw JA (1994) “Combination of multiple searches, the 2nd text retrieval conference (TREC-2)”, NIST Special Publication 500–215, pp 243–252Google Scholar
  13. Friggeri A, Chelius G, Fleury E (2011) Triangles to capture social cohesion CoRR abs/1107.323Google Scholar
  14. Gao M, Liu K, Wu Z (2010) Personalisation in web computing and informatics: theories, techniques, applications, and future research. Inf Syst Frontiers 12(5):607–629CrossRefGoogle Scholar
  15. Gauch S, Mirco S, Aravind C, Alessandro M (2007) “User profiles for personalized information access”. In: The adaptive web, vol. 4321, pp 54–89Google Scholar
  16. Goffman E (1959) “The presentation of self in everyday life” Garden City, NY, 2002Google Scholar
  17. Hubert G, Loiseau Y, Mothe J (2007) Etude de différentes fonctions de fusion de systèmes de recherche d’information, CIDE 10: Nancy, 02/07/2007-04/07/2007, EUROPIA, pp 199–207Google Scholar
  18. Kautz H, Selman B, Shah M (1997) Referral web: combining social networks and collaborative filtering. Commun ACM 40:3CrossRefGoogle Scholar
  19. Ley M (2009) DBLP—some lessons learned. PVLDB 2(2):1493–1500MathSciNetGoogle Scholar
  20. Masrden PV (2002) “Egocentric and sociocentric measures of network centrality”, Social networks, Vol 24, No 4 pp 407–422Google Scholar
  21. Massa P, Avesani P (2007) “Trust-aware recommender systems”. In: Proceedings of the 2007 ACM conference on recommender systems (RecSys ‘07). ACM, New York, NY, USA, pp 17–24Google Scholar
  22. Ren X, Zeng Y, Qin Y, Zhong N, Huang Z, Wang Y, Wang C (2010) Social relation based search refinement: let your friends help you!, International conferences on active media technology, AMT 2010 pp 475–485Google Scholar
  23. Salton G, Waldstein RK (1978) Term relevance weights in on-line information retrieval. Inf Process Manage 14(1):29–35CrossRefGoogle Scholar
  24. Sinha R, Swearingen K (2001) “Comparing recommendations made by online systems and friends” In: DELOS-NSF workshop on personalization and recommender systems in digital librariesGoogle Scholar
  25. Tchuente D, Canut CMF, Jessel NB, Péninou A, Haddadi AE (2010) “Visualizing the evolution of users’ profiles from online social networks”. In: ASONAM 2010, Odense–Denmark pp 370–374Google Scholar
  26. Tchuente D, Canut CMF, Jessel NB, Péninou A, Sedes F (2012) “Visualizing the relevance of social ties in user profile modeling”. WIAS (Web Intelligence and Agent Systems) An international journal 10(2):261–274Google Scholar
  27. Zeng Y, Yao YY, Zhong N (2009) DBLP-sse: A DBLP search support engine. In: Proceedings of the 2009 IEEE/WIC/ACM international conference on web intelligence, pp 626–630Google Scholar

Copyright information

© Springer-Verlag Wien 2013

Authors and Affiliations

  • Dieudonné Tchuente
    • 1
    Email author
  • Marie-Francoise Canut
    • 1
  • Nadine Jessel
    • 1
  • André Peninou
    • 1
  • Florence Sèdes
    • 1
  1. 1.IRIT, University of ToulouseToulouseFrance

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