Algorithms for clustering data
Algorithms for clustering data
Propagation of trust and distrust
Proceedings of the 13th international conference on World Wide Web
The link-prediction problem for social networks
Journal of the American Society for Information Science and Technology
Harvesting with SONAR: the value of aggregating social network information
Proceedings of the SIGCHI Conference on Human Factors in Computing Systems
Personalized recommendation of social software items based on social relations
Proceedings of the third ACM conference on Recommender systems
The WEKA data mining software: an update
ACM SIGKDD Explorations Newsletter
Same places, same things, same people?: mining user similarity on social media
Proceedings of the 2010 ACM conference on Computer supported cooperative work
Predicting positive and negative links in online social networks
Proceedings of the 19th international conference on World wide web
Bridging the gap between physical location and online social networks
Proceedings of the 12th ACM international conference on Ubiquitous computing
Structural Predictors of Tie Formation in Twitter: Transitivity and Mutuality
SOCIALCOM '10 Proceedings of the 2010 IEEE Second International Conference on Social Computing
Exploiting place features in link prediction on location-based social networks
Proceedings of the 17th ACM SIGKDD international conference on Knowledge discovery and data mining
Human mobility, social ties, and link prediction
Proceedings of the 17th ACM SIGKDD international conference on Knowledge discovery and data mining
Structural link analysis and prediction in microblogs
Proceedings of the 20th ACM international conference on Information and knowledge management
Acquaintance or partner?: predicting partnership in online and location-based social networks
Proceedings of the 2013 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining
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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.