188/4 E-Commerce Group
Institute of Software Technology and Interactive Systems
Vienna University of Technology
Favoritenstrasse 9-11/188, A-1040 Vienna, Austria

Exploring supervised methods for temporal link prediction in heterogeneous social networks

Type: 
Speech with proceedings
Abstract: 
In the link prediction problem, formulated as a binary classification problem, we want to classify each pair of disconnected nodes in the network whether they will be connected by a link in the future.<br> We study link formation in social networks with two types of links over several time periods. To solve the link prediction problem, we follow the approach of counting 3-node graphlets and suggest three extensions to the original method. By performing experiments on two real-world social networks we show that the new methods have a predictive power, however, network evolution cannot be explained by one specific feature at all time points. We also observe that some network properties can point at features which are more effective for temporal link prediction.
Hidden Keywords: 
Department Focus: 
Business Informatics
TU Focus: 
Proceedings: 
WWW 2015 Companion
Publisher: 
Year: 
2015
ISBN: 
Pages: 
1363 - 1368
Accepted: 
Acceptance undecided
Invited: 
Reference: 
<u>N. Rümmele</u>, R. Ichise, H. Werthner: <br>"<i><a href="http://dx.doi.org/10.1145/2740908.274169" target="_blank" class="publist">Exploring supervised methods for temporal link prediction in heterogeneous social networks</a></i>"; <br>Talk: 5th Temporal Web Analytics Worksho, Florence, Italy; 05-18-2015 - 05-22-2015; in: "<i>WWW 2015 Companion</i>", (2015), 1363 - 1368.<br><br> <a href="https://publik.tuwien.ac.at/showentry.php?ID=238251&lang=2" class="publist"><i>More information</i></a><br><br>
Abstract German: