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Link prediction addresses the problem of finding potential links that may form in the future. Existing state of art techniques exploit network topology for computing probability of future link formation. We are interested in using Graphical models for link prediction. Graphical models use higher order topological information underlying a graph for computing Co-occurrence probability of the nodes pertaining...
Many problems in the real world are, in general modeled as binary classification problems and often one class samples outnumber other class samples. This imbalance causes the reduction in accuracy of prediction in minority class samples but give overall high accuracy. Ignoring misclassification rate of minority class causes severe problems in many cases such as fraudulent credit card transactions,...
Social networks consisting of edges annotated with multiple links are natural models for real-world networks and pose a challenge for network analysis. Link prediction, predicting future links or missing links in a multi-relational network, is an important task from applications perspective. In large networks, time and memory are major constraints for link prediction. In this context, an algorithm...
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