Activity monitoring: noticing interesting changes in behavior
KDD '99 Proceedings of the fifth ACM SIGKDD international conference on Knowledge discovery and data mining
Learning from Labeled and Unlabeled Data using Graph Mincuts
ICML '01 Proceedings of the Eighteenth International Conference on Machine Learning
Learning relational probability trees
Proceedings of the ninth ACM SIGKDD international conference on Knowledge discovery and data mining
Measuring ISP topologies with rocketfuel
IEEE/ACM Transactions on Networking (TON)
The link-prediction problem for social networks
Journal of the American Society for Information Science and Technology
Statistical Comparisons of Classifiers over Multiple Data Sets
The Journal of Machine Learning Research
Centrality Measures from Complex Networks in Active Learning
DS '09 Proceedings of the 12th International Conference on Discovery Science
Combining Local and Global KNN With Cotraining
Proceedings of the 2010 conference on ECAI 2010: 19th European Conference on Artificial Intelligence
A nonparametric classification method based on K-associated graphs
Information Sciences: an International Journal
LPmade: Link Prediction Made Easy
The Journal of Machine Learning Research
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Cluster in graphs is densely connected group of vertices sparsely connected to other groups. Hence, for prediction of a future link between a pair of vertices, these vertices common neighbors may play different roles depending on if they belong or not to the same cluster. Based on that, we propose a new measure (WIC) for link prediction between a pair of vertices considering the sets of their intra-cluster or within-cluster (W) and between-cluster or inter-cluster (IC) common neighbors. Also, we propose a set of measures, referred to as W forms, using only the set given by the within-cluster common neighbors instead of using the set of all common neighbors as usually considered in the basic local similarity measures. Consequently, a previous clustering scheme must be applied on the graph. Using three different clustering algorithms, we compared WIC measure with ten basic local similarity measures and their counterpart W forms on ten real networks. Our analyses suggest that clustering information, no matter the clustering algorithm used, improves link prediction accuracy.