Referral Web: combining social networks and collaborative filtering
Communications of the ACM
IDA '01 Proceedings of the 4th International Conference on Advances in Intelligent Data Analysis
The link-prediction problem for social networks
Journal of the American Society for Information Science and Technology
Recovering temporally rewiring networks: a model-based approach
Proceedings of the 24th international conference on Machine learning
Temporal-Relational Classifiers for Prediction in Evolving Domains
ICDM '08 Proceedings of the 2008 Eighth IEEE International Conference on Data Mining
The SAGE Handbook of Social Network Analysis
The SAGE Handbook of Social Network Analysis
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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.