The anatomy of a large-scale hypertextual Web search engine
WWW7 Proceedings of the seventh international conference on World Wide Web 7
Rank aggregation methods for the Web
Proceedings of the 10th international conference on World Wide Web
Proceedings of the 24th annual international ACM SIGIR conference on Research and development in information retrieval
Efficient similarity search and classification via rank aggregation
Proceedings of the 2003 ACM SIGMOD international conference on Management of data
The link prediction problem for social networks
CIKM '03 Proceedings of the twelfth international conference on Information and knowledge management
Link prediction approach to collaborative filtering
Proceedings of the 5th ACM/IEEE-CS joint conference on Digital libraries
Proceedings of the 16th international conference on World Wide Web
IEEE Transactions on Knowledge and Data Engineering
A Short Introduction to Computational Social Choice
SOFSEM '07 Proceedings of the 33rd conference on Current Trends in Theory and Practice of Computer Science
Link Prediction on Evolving Data Using Matrix and Tensor Factorizations
ICDMW '09 Proceedings of the 2009 IEEE International Conference on Data Mining Workshops
Supervised Machine Learning Applied to Link Prediction in Bipartite Social Networks
ASONAM '10 Proceedings of the 2010 International Conference on Advances in Social Networks Analysis and Mining
Temporal link prediction by integrating content and structure information
Proceedings of the 20th ACM international conference on Information and knowledge management
CRF framework for supervised preference aggregation
Proceedings of the 22nd ACM international conference on Conference on information & knowledge management
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In this paper we propose a new topological approach for link prediction in dynamic complex networks. The proposed approach applies a supervised rank aggregation method. This functions as follows: first we rank the list of unlinked nodes in a network at instant t according to different topological measures (nodes characteristics aggregation, nodes neighborhood based measures, distance based measures, etc). Each measure provides its own rank. Observing the network at instant t+1 where some new links appear, we weight each topological measure according to its performances in predicting these observed new links. These learned weights are then used in a modified version of classical computational social choice algorithms (such as Borda, Kemeny, etc) in order to have a model for predicting new links. We show the effectiveness of this approach through different experimentations applied to co-authorship networks extracted from the DBLP bibliographical database. Results we obtain, are also compared with the outcome of classical supervised machine learning based link prediction approaches applied to the same datasets.