Abstract
There has been a considerable amount of recent research on the link prediction problem, that is, the problem of accurately predicting edges that will be established between actors in a social network in a future period. With the cooperation of the provider of a German social network site (SNS), we aim to contribute to this line of research by analyzing the link formation and interaction patterns of approximately 9.38 million members of one of the largest German online social networks (OSN). It is our goal to explore the value of users’ interaction frequencies for link prediction based on metrics of local structural similarity. Analyzing a random sample of the network, we found that only a portion of the network is responsible for most of the activity observed: 42.64 % of the network’s population account for all observed interactions and 25.33 % are responsible for all private communication. We have also established that the degree of recent interaction is positively correlated with imminent link formation – users with high interaction frequencies are more likely to establish new friendships. The evaluation of our link prediction approach yields results that are consistent with comparable studies. Traditional metrics seem to outperform weighted metrics that account for interaction frequencies. We conclude that while weighted metrics tend to predict strong ties, users of SNS establish both strong and weak ties. Our findings indicate that members of an SNS prefer quantity over quality in terms of establishing new connections. In our case, this causes the simplest metrics to perform best.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Notes
- 1.
While we stick to the notation WJC because of the analogy to JC, we note that the JC metric has a more complex background. This version does not necessarily comply with its initial intention. We are interested primarily in the analogy of its interpretation, and hence this is merely a formal issue and of no further relevance for our work.
- 2.
The SNS we analyzed enables private communication between users by providing an integrated direct messaging system through which users exchange text messages via a web-based interface.
- 3.
References
Adamic L (2003) Friends and neighbors on the Web. Soc Networks 25(3):211–230
Backstrom L, Huttenlocher D, Kleinberg J, Lan X (2006) Group formation in large social networks: membership, growth, and evolution. In: Proceedings of the 12th ACM SIGKDD international conference on Knowledge discovery and data mining, ACM, New York, pp 44–54
Backstrom L, Leskovec J (2011) Supervised random walks: predicting and recommending links in social networks. In: Proceedings of the fourth ACM international conference on Web search and data mining, ACM, New York, pp 635–644
Barabasi A-L, Albert R (1999) Emergence of scaling in random networks. Science 286(5439):11
Börner K, Sanyal S, Vespignani A (2008) Network science. Annu Rev Inform Sci Technol 41(1):537–607
Boyd DM, Ellison NB (2007) Social network sites: definition, history, and scholarship. J Comput-Mediat Commun 13(1):210–230
Donath J, Boyd D (2004) Public displays of connection. BT Technol J 22(4):71–82
Fawcett T (2006) An introduction to ROC analysis. Pattern Recogn Lett 27(8):861–874
Gilbert E, Karahalios K (2009) Predicting tie strength with social media. In: Proceedings of the 27th international conference on human factors in computing systems – CHI ‘09, ACM, New York, p 211
Granovetter M (1983) The strength of weak ties: a network theory revisited. Social Theory 1:201
Hill R, Dunbar RIM (2003) Social network size in humans. Hum Nat 14(1):53–72
Kossinets G, Watts DJ (2006) Empirical analysis of an evolving social network. Science (New York, NY) 311(5757):88–90
Kumar R, Novak J, Tomkins A (2006) Structure and evolution of online social networks. In: Proceedings of the 12th ACM SIGKDD international conference on knowledge discovery and data mining – KDD ‘06, ACM, New York, p 611
Leskovec J, Backstrom L, Kumar R, Tomkins A (2008) Microscopic evolution of social networks. In: Proceeding of the 14th ACM SIGKDD international conference on knowledge discovery and data mining – KDD ‘08, ACM Press, New York, p 462
Liben-Nowell D, Kleinberg J (2007) The link-prediction problem for social networks. J Am Soc Inf Sci Technol 58(7):1019–1031
Lü L, Zhou T (2010) Link-prediction in weighted networks: the role of weak ties. EPL (Europhys Lett) 89(1):18001
Lü L, Zhou T (2011) Link-prediction in complex networks: a survey. Physica A Stat Mech Appl 390(6):1150–1170
Murata T, Moriyasu S (2008) Link-prediction based on Structural Properties of Online Social Networks. N Gener Comput 26(3):245–257
Newman MEJ (2001) Clustering and preferential attachment in growing networks. Phys Rev E 64(2):1–4
Newman MEJ (2001) The structure of scientific collaboration networks. Proc Natl Acad Sci U S A 98(2):404–409
Newman MEJ (2003) Mixing patterns in networks. Phys Rev E 67(2):1–13
Newman MEJ, Park J (2003) Why social networks are different from other types of networks. Phys Rev E 68(3):1–8
Panzarasa P, Opsahl T (2009) Patterns and dynamics of users behavior and interaction: network analysis of an online community. J Am Soc Inf Sci Technol 60(5):911–932
Roth M, Ben-David A, Deutscher D (2010) Suggesting friends using the implicit social graph. In: Proceedings of the KDD ‘10, ACM, New York, pp 233–241
Tylenda T, Angelova R, Bedathur S (2009) Towards time-aware link-prediction in evolving social networks. In: Proceedings of the 3rd workshop on social network mining and analysis – SNA-KDD ‘09, ACM Press, New York, pp 1–10
Watts DJ (2004) The “new” science of networks. Annu Rev Sociol 30(1):243–270
Watts DJ (2007) A twenty-first century science. Nature 445(7127):489
Wilson C, Boe B, Sala A, Puttaswamy KP, Zhao BY (2009) User interactions in social networks and their implications. In: Proceedings of the fourth ACM European conference on computer systems – EuroSys ‘09, Nuremberg, p 205
Xiang EW (2008) A survey on link prediction models for social network data. Ph.D. Dissertation, Department of Computer Science and Engineering, The Hong Kong University of Science and Technology
Zhou T, Lü L, Zhang Y-C (2009) Predicting missing links via local information. Euro Phys J B 71(4):623–630
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2014 Springer International Publishing Switzerland
About this paper
Cite this paper
Posegga, O., Fischbach, K., Donath, M. (2014). Using Weighted Interaction Metrics for Link Prediction in a Large Online Social Network. In: Zweig, K., Neuser, W., Pipek, V., Rohde, M., Scholtes, I. (eds) Socioinformatics - The Social Impact of Interactions between Humans and IT. Springer Proceedings in Complexity. Springer, Cham. https://doi.org/10.1007/978-3-319-09378-9_5
Download citation
DOI: https://doi.org/10.1007/978-3-319-09378-9_5
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-09377-2
Online ISBN: 978-3-319-09378-9
eBook Packages: Computer ScienceComputer Science (R0)