Skip to main content
SpringerLink
Log in

Detecting partnership in location-based and online social networks

  • Original Article
  • Published:
Social Network Analysis and Mining Aims and scope Submit manuscript

Abstract

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.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Fig. 1

Similar content being viewed by others

Notes

  1. http://aws.amazon.com/cloudfront/.

  2. http://secondlife.com/community/events/.

  3. http://lib.openmetaverse.org/.

References

  • Adamic LA, Adar E (2003) Friends and neighbors on the web. Soc Netw 25(3):211–230

    Article  Google Scholar 

  • Aral S, Walker D (2012) Identifying influential and susceptible members of social networks. Science 337(6092):337–341

    Article  MathSciNet  Google Scholar 

  • 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 831–841

  • Bainbridge WS (2007) The scientific research potential of virtual worlds. Science 317(5837):472–476

    Article  Google Scholar 

  • Barabási AL, Albert R (1999) Emergence of scaling in random networks. Science 286(5439):509–512

    Article  MathSciNet  Google Scholar 

  • 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 47–56

  • 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 49–56

  • 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 295–302

  • Coleman JS (1988) Social capital in the creation of human capital. Am J Sociol 94:S95–S120

    Article  Google Scholar 

  • 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 119–128

  • 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 73–80

  • 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):10:1–10:25

  • Gilbert E (2012) Predicting tie strength in a new medium. In: Proceedings of the ACM 2012 conference on computer supported cooperative work. ACM, pp 1047–1056

  • 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 211–220

  • Granovetter MS (1973) The strength of weak ties. Am J Sociol 78(6):1360–1380

    Article  Google Scholar 

  • 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):304–308

    Article  Google Scholar 

  • 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):10–18

    Article  Google Scholar 

  • Huang J, Ling CX (2005) Using auc and accuracy in evaluating learning algorithms. IEEE Trans Knowl Data Eng 17(3):299–310

    Article  Google Scholar 

  • 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):1–13

    Article  Google Scholar 

  • Liben-Nowell D, Kleinberg J (2002) The link-prediction problem for social networks. J Am Soc Inf Sci Technol 58(7):1019–1031

    Article  Google Scholar 

  • 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 519–526

  • 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 251–260

  • 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 1038–1043

  • 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 207–214

  • 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 476–491

  • Scellato S, Noulas A, Lambiotte R, Mascolo C (2011a) Socio-spatial properties of online location-based social networks. Proc ICWSM 11:329–336

    Google Scholar 

  • 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 1046–1054

  • 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 5:1–5:8

  • 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 108–127

  • Steurer M, Trattner C, Kappe F (2012) Success factors of events in virtual worlds a case study in second life. In: NetGames, pp 1–2

  • Szell M, Sinatra R, Petri G, Thurner S, Latora V (2012) Understanding mobility in a social petri dish. Sci Rep 2:457

    Article  Google Scholar 

  • Thelwall M (2009) Homophily in myspace. J Am Soc Inf Sci Technol 60(2):219–231

    Article  Google Scholar 

  • 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 1100–1108

  • 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 97–113

Download references

Acknowledgments

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).

Author information

Authors and Affiliations

  1. NTNU, Trondheim, Norway

    Christoph Trattner

  2. Know-Center, Graz, Austria

    Christoph Trattner

  3. IICM, Graz University of Technology, Graz, Austria

    Michael Steurer

Authors
  1. Christoph Trattner
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Michael Steurer
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Christoph Trattner.

Additional information

This article is based on the paper “Acquaintance or Partner? Predicting Partnership in Online and Location-based Social Networks” published in the Proceedings of the 2013 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Trattner, C., Steurer, M. Detecting partnership in location-based and online social networks. Soc. Netw. Anal. Min. 5, 42 (2015). https://doi.org/10.1007/s13278-015-0281-1

Download citation

  • Received:

  • Revised:

  • Accepted:

  • Published:

  • DOI: https://doi.org/10.1007/s13278-015-0281-1

Keywords

Search

Navigation