Detecting partnership in location-based and online social networks

  • Christoph Trattner
  • Michael Steurer
Original Article


Existing approaches to identify the tie strength between users involve typically only one type of network. To date, no studies exist that investigate the intensity of social relations and in particular partnership between users across social networks. To fill this gap in the literature, we studied over 50 social proximity features to detect the tie strength of users defined as partnership in two different types of networks: location-based and online social networks. We compared user pairs in terms of partners and non-partners and found significant differences between those users. Following these observations, we evaluated the social proximity of users via supervised and unsupervised learning approaches and establish that location-based social networks have a great potential for the identification of a partner relationship. In particular, we established that location-based social networks and correspondingly induced features based on events attended by users could identify partnership with 0.922 AUC, while online social network data had a classification power of 0.892 AUC. When utilizing data from both types of networks, a partnership could be identified to a great extent with 0.946 AUC. This article is relevant for engineers, researchers and teachers who are interested in social network analysis and mining.


Online social networks Location-based social networks Partner detection Virtual worlds 



This work is supported by the Know-Center and the EU funded projects Learning Layers (Grant Agreement 318209). Moreover, parts of this work were carried out during the tenure of an ERCIM “Alain Bensoussan” fellowship program by the first author. The Know-Center is funded within the Austrian COMET Program—Competence Centers for Excellent Technologies—under the auspices of the Austrian Ministry of Transport, Innovation and Technology, the Austrian Ministry of Economics and Labor and by the State of Styria. COMET is managed by the Austrian Research Promotion Agency (FFG).


  1. Adamic LA, Adar E (2003) Friends and neighbors on the web. Soc Netw 25(3):211–230CrossRefGoogle Scholar
  2. Aral S, Walker D (2012) Identifying influential and susceptible members of social networks. Science 337(6092):337–341MathSciNetCrossRefGoogle Scholar
  3. Backstrom L, Kleinberg J (2014) Romantic partnerships and the dispersion of social ties: a network analysis of relationship status on facebook. In: Proceedings of the 17th ACM conference on computer supported cooperative work & social computing. ACM, pp 831–841Google Scholar
  4. Bainbridge WS (2007) The scientific research potential of virtual worlds. Science 317(5837):472–476CrossRefGoogle Scholar
  5. Barabási AL, Albert R (1999) Emergence of scaling in random networks. Science 286(5439):509–512MathSciNetCrossRefGoogle Scholar
  6. Bischoff K (2012) We love rock’n’roll: analyzing and predicting friendship links in Last. fm. In: Proceedings of the 3rd annual ACM web science conference. ACM, pp 47–56Google Scholar
  7. Cheng J, Romero DM, Meeder B, Kleinberg J (2011) Predicting reciprocity in social networks. In: 2011 IEEE third international conference on privacy, security, risk and trust (PASSAT), and 2011 IEEE third international conference on social computing (SocialCom). IEEE, pp 49–56Google Scholar
  8. Choi J, Heo S, Han J, Lee G, Song J (2013) Mining social relationship types in an organization using communication patterns. In: Proceedings of the 2013 conference on computer supported cooperative work. ACM, pp 295–302Google Scholar
  9. Coleman JS (1988) Social capital in the creation of human capital. Am J Sociol 94:S95–S120CrossRefGoogle Scholar
  10. Cranshaw J, Toch E, Hong J, Kittur A, Sadeh N (2010) Bridging the gap between physical location and online social networks. In: Proceedings of the 12th ACM international conference on Ubiquitous computing. ACM, pp 119–128Google Scholar
  11. Fire M, Tenenboim L, Lesser O, Puzis R, Rokach L, Elovici Y (2011) Link prediction in social networks using computationally efficient topological features. In: 2011 IEEE third international conference on privacy, security, risk and trust (passat), 2011 IEEE third international conference on social computing (socialcom). IEEE, pp 73–80Google Scholar
  12. Fire M, Tenenboim-Chekina L, Puzis R, Lesser O, Rokach L, Elovici Y (2014) Computationally efficient link prediction in a variety of social networks. ACM Trans Intell Syst Technol 5(1):10:1–10:25Google Scholar
  13. Gilbert E (2012) Predicting tie strength in a new medium. In: Proceedings of the ACM 2012 conference on computer supported cooperative work. ACM, pp 1047–1056Google Scholar
  14. Gilbert E, Karahalios K (2009) Predicting tie strength with social media. In: Proceedings of the SIGCHI conference on human factors in computing systems. ACM, pp 211–220Google Scholar
  15. Granovetter MS (1973) The strength of weak ties. Am J Sociol 78(6):1360–1380CrossRefGoogle Scholar
  16. Guadagno RE, Muscanell NL, Okdie BM, Burk NM, Ward TB (2011) Even in virtual environments women shop and men build: a social role perspective on second life. Comput Hum Behav 27(1):304–308CrossRefGoogle Scholar
  17. Hall M, Frank E, Holmes G, Pfahringer B, Reutemann P, Witten IH (2009) The WEKA data mining software: an update. ACM SIGKDD Explor Newsl 11(1):10–18CrossRefGoogle Scholar
  18. Huang J, Ling CX (2005) Using auc and accuracy in evaluating learning algorithms. IEEE Trans Knowl Data Eng 17(3):299–310CrossRefGoogle Scholar
  19. Kock N (2008) E-collaboration and e-commerce in virtual worlds: the potential of second life and world of warcraft. Int J e-Collab 4(3):1–13CrossRefGoogle Scholar
  20. Liben-Nowell D, Kleinberg J (2002) The link-prediction problem for social networks. J Am Soc Inf Sci Technol 58(7):1019–1031CrossRefGoogle Scholar
  21. Ling CX, Huang J, Zhang H (2003) AUC: a statistically consistent and more discriminating measure than accuracy. In: International joint conference on artificial intelligence. Lawrence Erlbaum Associates ltd, pp 519–526Google Scholar
  22. 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. ACM, pp 251–260Google Scholar
  23. Noulas A, Scellato S, Lathia N, Mascolo C (2012) Mining user mobility features for next place prediction in location-based services. In: 2012 IEEE 12th international conference on data mining (ICDM). IEEE, pp 1038–1043Google Scholar
  24. Pizzato L, Rej T, Chung, Koprinska I, Kay J (2010) Recon: a reciprocal recommender for online dating. In: Proceedings of the fourth ACM conference on recommender systems. ACM, pp 207–214Google Scholar
  25. Rowe M, Stankovic M, Alani H (2012) Who will follow whom? Exploiting semantics for link prediction in attention-information networks. In: Proceedings of the 11th international conference on the semantic web, ISWC’12, vol part I. Springer, Berlin, pp 476–491Google Scholar
  26. Scellato S, Noulas A, Lambiotte R, Mascolo C (2011a) Socio-spatial properties of online location-based social networks. Proc ICWSM 11:329–336Google Scholar
  27. Scellato S, Noulas A, Mascolo C (2011b) Exploiting place features in link prediction on location-based social networks. In: Proceedings of the 17th ACM SIGKDD international conference on knowledge discovery and data mining. ACM, pp 1046–1054Google Scholar
  28. Steurer M, Trattner C (2013) Predicting interactions in online social networks: an experiment in second life. In: Proceedings of the 4th international workshop on modeling social media, MSM ’13. ACM, New York, pp 5:1–5:8Google Scholar
  29. Steurer M, Trattner S (2013) Who will interact with whom? A case-study in second life using online social network and location-based social network features to predict interactions between users. In: Ubiquitous social media analysis. Springer, Berlin, pp 108–127Google Scholar
  30. Steurer M, Trattner C, Kappe F (2012) Success factors of events in virtual worlds a case study in second life. In: NetGames, pp 1–2Google Scholar
  31. Szell M, Sinatra R, Petri G, Thurner S, Latora V (2012) Understanding mobility in a social petri dish. Sci Rep 2:457CrossRefGoogle Scholar
  32. Thelwall M (2009) Homophily in myspace. J Am Soc Inf Sci Technol 60(2):219–231CrossRefGoogle Scholar
  33. Wang D, Pedreschi D, Song C, Giannotti F, Barabasi A-L (2011) Human mobility, social ties, and link prediction. In: Proceedings of the 17th ACM SIGKDD international conference on knowledge discovery and data mining. ACM, New York, pp 1100–1108Google Scholar
  34. Zheleva E, Getoor L, Golbeck J, Kuter U (2010) Using friendship ties and family circles for link prediction. In: Advances in social network mining and analysis. Springer, Berlin, pp 97–113Google Scholar

Copyright information

© Springer-Verlag Wien 2015

Authors and Affiliations

  1. 1.NTNUTrondheimNorway
  2. 2.Know-CenterGrazAustria
  3. 3.IICM, Graz University of TechnologyGrazAustria

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