Visual analytics of social networks: mining and visualizing co-authorship networks
FAC'11 Proceedings of the 6th international conference on Foundations of augmented cognition: directing the future of adaptive systems
A supervised machine learning link prediction approach for tag recommendation
OCSC'11 Proceedings of the 4th international conference on Online communities and social computing
Link prediction for annotation graphs using graph summarization
ISWC'11 Proceedings of the 10th international conference on The semantic web - Volume Part I
Friendship prediction and homophily in social media
ACM Transactions on the Web (TWEB)
Supervised rank aggregation approach for link prediction in complex networks
Proceedings of the 21st international conference companion on World Wide Web
Using co-visitation networks for detecting large scale online display advertising exchange fraud
Proceedings of the 19th ACM SIGKDD international conference on Knowledge discovery and data mining
Dense subgraph mining with a mixed graph model
Pattern Recognition Letters
Internal link prediction: A new approach for predicting links in bipartite graphs
Intelligent Data Analysis - Dynamic Networks and Knowledge Discovery
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This work copes with the problem of link prediction in large-scale two-mode social networks. Two variations of the link prediction tasks are studied: predicting links in a bipartite graph and predicting links in a unimodal graph obtained by the projection of a bipartite graph over one of its node sets. For both tasks, we show in an empirical way, that taking into account the bipartite nature of the graph can enhance substantially the performances of prediction models we learn. This is achieved by introducing new variations of topological atttributes to measure the likelihood of two nodes to be connected. Our approach, for both tasks, consists in expressing the link prediction problem as a two class discrimination problem. Classical supervised machine learning approaches can then be applied in order to learn prediction models. Experimental validation of the proposed approach is carried out on two real data sets: a co-authoring network extracted from the DBLP bibliographical database and bipartite graph history of transactions on an on-line music e-commerce site.