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etworks have become ubiquitous. Communication networks, financial transaction networks, networks describing physical systems, and social networks are all becoming increasingly important in our day-today life. Often, we are interested in models of how nodes in the network influence each other (for example, who infects whom in an epidemiological network),(More)
The problems of object classification (labeling the nodes of a graph) and link prediction (predicting the links in a graph) have been largely studied independently. Commonly , object classification is performed assuming a complete set of known links and link prediction is done assuming a fully observed set of node attributes. In most real world domains,(More)
We address the cost-sensitive feature acquisition problem, where misclassifying an instance is costly but the expected misclassification cost can be reduced by acquiring the values of the missing features. Because acquiring the features is costly as well, the objective is to acquire the right set of features so that the sum of the feature acquisition cost(More)
Supervised and semi-supervised data mining techniques require labeled data. However, labeling examples is costly for many real-world applications. To address this problem, active learning techniques have been developed to guide the labeling process in an effort to minimize the amount of labeled data without sacrificing much from the quality of the learned(More)
Visualizing and analyzing social networks is a challenging problem that has been receiving growing attention. An important first step, before analysis can begin, is ensuring that the data is accurate. A common data quality problem is that the data may inadvertently contain several distinct references to the same underlying entity; the process of reconciling(More)