Using network evolution theory and singular value decomposition method to improve accuracy of link prediction in social networks

Publisher:
Australian Computer Society
Publication Type:
Conference Proceeding
Citation:
Proceedings of the Tenth Australasian Data Mining Conference (AusDM-12),, 2012, pp. 175 - 181
Issue Date:
2012-01
Full metadata record
Files in This Item:
Filename Description Size
Thumbnail2011007485OK.pdf1.31 MB
Adobe PDF
Link prediction in large networks, especially social networks, has received significant recent attention. Although there are many papers contributing methods for link prediction, the accuracy of most predictors is generally low as they treat all nodes equally. We propose an effective approach to identifying the level of activities of nodes in networks by observing their behaviour during network evolution. It is clear that nodes that have been active previously contribute more to the changes in a network than stable nodes, which have low activity. We apply truncated singular value decomposition (SVD) to exclude the interference of stable nodes by treating them as noise in our dataset. Finally, in order to test the effectiveness of our proposed method, we use co-authorship networks from an Australian university from between 2006 and 2011 as an experimental dataset. The results show that our proposed method achieves higher accuracy in link prediction than previous methods, especially in predicting new links.
Please use this identifier to cite or link to this item: