acquaintance or partner? predicting partnership in online and location-based social networks
DESCRIPTION
Existing approaches to predict tie strength between users cover either online social networks or location-based social networks. However, there are no studies that combine these networks to unveil information about the intensity of the social relations between users. In this research paper we analyze aspects of tie strength – defined as partners and acquaintances – from two different domains: a location-based social network and an online social network for residents of Second Life. We compare user pairs according to their partnership and reveal significant differences between partners and acquaintances. Following these observations, we evaluate the social proximity of users with supervised and unsupervised learning algorithms and identified homophilic features as most valuable for the prediction of partnership.TRANSCRIPT
Information Systems and Computer Media - Graz University of Technology!
Predicting Partnership in Online and Location-Based Social Networks!
Acquaintance or Partner? !!
Predicting Partnership in Online and !Location-Based Social Networks!
Michael Steurer and Christoph Trattner!Graz University of Technology!
Austria!
Information Systems and Computer Media - Graz University of Technology!
Predicting Partnership in Online and Location-Based Social Networks!
Introduction!! Partner vs. Acquaintance!
! Tie strength!! Combined Data from Second Life!
! Online social network!! Location-based social network!! Compute Features!
! Experiments!! Compare differences!! Predictability of partnership!
Michael Steurer!!
Information Systems and Computer Media - Graz University of Technology!
Predicting Partnership in Online and Location-Based Social Networks!
Second Life!
http://notizen.typepad.com/aus_der_provinz/sl061018_001_1.jpg!Michael Steurer!
!
Information Systems and Computer Media - Graz University of Technology!
Predicting Partnership in Online and Location-Based Social Networks!
Event Data!
Michael Steurer!!
Information Systems and Computer Media - Graz University of Technology!
Predicting Partnership in Online and Location-Based Social Networks!
Position Data!
Michael Steurer!!
Information Systems and Computer Media - Graz University of Technology!
Predicting Partnership in Online and Location-Based Social Networks!
Location-Based Data!
! Event Data!! 12 months starting in March 2012!! 262,234 unique events !
! Location-based Social Data!! 4,105 unique regions!! 19 Million data samples!! 410,619 unique users !
Michael Steurer!!
Information Systems and Computer Media - Graz University of Technology!
Predicting Partnership in Online and Location-Based Social Networks!
Online Social Data!
Michael Steurer!!
Information Systems and Computer Media - Graz University of Technology!
Predicting Partnership in Online and Location-Based Social Networks!
Online Social Data!! Online Social Data!
! 152,509 unique users!! 1,084,002 Postings (text messages, snapshots)!! 459,734 Comments!! 1,631,568 Loves!
! Groups and Interests!! 285,528 unique groups!! 15.51 groups per user on average!! 6.5 interests per user on average!
Michael Steurer!!
Information Systems and Computer Media - Graz University of Technology!
Predicting Partnership in Online and Location-Based Social Networks!
Partnership!
Michael Steurer!!
Information Systems and Computer Media - Graz University of Technology!
Predicting Partnership in Online and Location-Based Social Networks!
Partnership!
! Partnership!! 39,468 users (out of 152,509)!
! Compare to Real Life!! Marriage – 10 Linden Dollars!! Divorce – 25 Linden Dollars!
Michael Steurer!!
Information Systems and Computer Media - Graz University of Technology!
Predicting Partnership in Online and Location-Based Social Networks!
Networks!! Created Networks!
! Online social network!! Location-based social network!! Intersection!
! Topological Features!! Common Neighbors, Adamic-Adar, …!
! Homophilic Features!! Groups, regions, distance, …!
Michael Steurer!!
Information Systems and Computer Media - Graz University of Technology!
Predicting Partnership in Online and Location-Based Social Networks!
Experimental Setup!! Merged Networks + Features!
! Users available in both networks!! 44,603 users and 1,419,543 edges!! 3,096 users in partnership!!
! Partner or Acquaintances!! Differences!! Predictability!
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Michael Steurer!!
Information Systems and Computer Media - Graz University of Technology!
Predicting Partnership in Online and Location-Based Social Networks!
Partner vs. Acquaintance!
! Compare User-Pairs!! In a relationship!! With interaction in online social network!
! Measures and Analysis!! Mean values !! Significance tests!
Michael Steurer!!
Information Systems and Computer Media - Graz University of Technology!
Predicting Partnership in Online and Location-Based Social Networks!
Online Social Network!
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Michael Steurer!!
Information Systems and Computer Media - Graz University of Technology!
Predicting Partnership in Online and Location-Based Social Networks!
Location-based Social Network!
***p<0.001!
Michael Steurer!!
Information Systems and Computer Media - Graz University of Technology!
Predicting Partnership in Online and Location-Based Social Networks!
Predict Partnership!
! Balanced Dataset 3,000 User-Pairs!! 1,500 partners and 1,500 acquaintances!! Random guessing ⇒ 50%!
! Area Under the ROC Curve (AUC)!! Logistic Regression, SVM, Random Forest!
Michael Steurer!!
Information Systems and Computer Media - Graz University of Technology!
Predicting Partnership in Online and Location-Based Social Networks!
Supervised Learning!
Michael Steurer!!
Information Systems and Computer Media - Graz University of Technology!
Predicting Partnership in Online and Location-Based Social Networks!
Feature Analysis!
! Information Gain and AUC!! Collaborative Filtering!
! Online Social Network!! Topological Features!
! Location Based Social Network!! Homophilic Features!
Michael Steurer!!
Information Systems and Computer Media - Graz University of Technology!
Predicting Partnership in Online and Location-Based Social Networks!
Feature Analysis!
Michael Steurer!!
Information Systems and Computer Media - Graz University of Technology!
Predicting Partnership in Online and Location-Based Social Networks!
Conclusion!! Large Dataset of Different Domains!
! Online Social and Location-Based!! Same Users!
! Computed ‘Proximity’ Between Users!! Topological!! Homophilic!
! Significant Differences!! Predict Partnership with 93.3%!
Michael Steurer!!
Information Systems and Computer Media - Graz University of Technology!
Predicting Partnership in Online and Location-Based Social Networks!
Acquaintance or Partner? !!
Predicting Partnership in Online and !Location-Based Social Networks!
Michael Steurer and Christoph Trattner!Graz University of Technology!
Austria!