Using Non-topological Node Attributes to Improve Results of Link Prediction in Social Networks

Abstract

This paper examines the importance of non-topological node attributes for link prediction in social networks. Rank method and supervised learning method were introduced to show the role of the node attributes in link prediction respectively. A rule for choosing the appropriate node attributes was discussed and a method for aggregating two node attributes was proposed. The result of the experiments on a blog dataset showed that using non-topological node attributes make a better performance in link prediction.