Predicting interactions in online social networks: an experiment in Second Life

Although considerable amount of work has been conducted recently of how to predict links between users in online social media, studies exploiting different kinds of knowledge sources for the link prediction problem are rare. In this paper latest results of a project are presented that studies the extent to which interactions -- in our case directed and bi-directed message communication -- between users in online social networks can be predicted by looking at features obtained from social network and position data. To that end, we conducted two experiments in the virtual world of Second Life. As our results reveal, position data features are a great source to predict interacts between users in online social networks and outperform social network features significantly. However, if we try to predict reciprocal message communication between users, social network features seem to be superior.

[1]  Christoph Trattner,et al.  Success factors of events in virtual worlds a case study in Second Life , 2012, 2012 11th Annual Workshop on Network and Systems Support for Games (NetGames).

[2]  Aniket Kittur,et al.  Bridging the gap between physical location and online social networks , 2010, UbiComp.

[3]  Cecilia Mascolo,et al.  Exploiting place features in link prediction on location-based social networks , 2011, KDD.

[4]  Anil K. Jain,et al.  Algorithms for Clustering Data , 1988 .

[5]  Stephen Farrell,et al.  Harvesting with SONAR: the value of aggregating social network information , 2008, CHI.

[6]  Ramanathan V. Guha,et al.  Propagation of trust and distrust , 2004, WWW '04.

[7]  Brian D. Davison,et al.  Structural link analysis and prediction in microblogs , 2011, CIKM '11.

[8]  Dino Pedreschi,et al.  Human mobility, social ties, and link prediction , 2011, KDD.

[9]  Lada A. Adamic,et al.  Friends and neighbors on the Web , 2003, Soc. Networks.

[10]  Rami Puzis,et al.  Link Prediction in Social Networks Using Computationally Efficient Topological Features , 2011, 2011 IEEE Third Int'l Conference on Privacy, Security, Risk and Trust and 2011 IEEE Third Int'l Conference on Social Computing.

[11]  Jure Leskovec,et al.  Predicting positive and negative links in online social networks , 2010, WWW '10.

[12]  Matthew Rowe,et al.  Who Will Follow Whom? Exploiting Semantics for Link Prediction in Attention-Information Networks , 2012, SEMWEB.

[13]  Justin Cheng,et al.  Predicting Reciprocity in Social Networks , 2011, 2011 IEEE Third Int'l Conference on Privacy, Security, Risk and Trust and 2011 IEEE Third Int'l Conference on Social Computing.

[14]  Ian H. Witten,et al.  The WEKA data mining software: an update , 2009, SKDD.

[15]  Albert,et al.  Emergence of scaling in random networks , 1999, Science.

[16]  Ido Guy,et al.  Personalized recommendation of social software items based on social relations , 2009, RecSys '09.

[17]  Jon Kleinberg,et al.  The link prediction problem for social networks , 2003, CIKM '03.

[18]  Scott A. Golder,et al.  Structural Predictors of Tie Formation in Twitter: Transitivity and Mutuality , 2010, 2010 IEEE Second International Conference on Social Computing.

[19]  David Liben-Nowell,et al.  The link-prediction problem for social networks , 2007 .

[20]  Ido Guy,et al.  Same places, same things, same people?: mining user similarity on social media , 2010, CSCW '10.