This paper presents NetKit, a modular toolkit for classification in networked data, and a case-study of its application to a collection of networked data sets used in prior machine learning research. Networked data are relational data where entities are interconnected, and this paper considers the common case where entities whose labels are to be estimated are linked to entities for which the label is known. NetKit is based on a three-component framework, comprising a local classifier, a relational classifier, and a collective inference procedure. Various existing relational learning algorithms can be instantiated with appropriate choices for these three components and new relational learning algorithms can be composed by new combinations of components. The case study demonstrates how the toolkit facilitates comparison of different learning methods (which so far has been lacking in machine learning research). It also shows how the modular framework allows analysis of subcomponents, to assess which, whether, and when particular components contribute to superior performance. The case study focuses on the simple but important special case of univariate network classification, for which the only information available is the structure of class linkage in the network (i.e., only links and some class labels are available). To our knowledge, no work previously has evaluated systematically the power of class-linkage alone for classification in machine learning benchmark data sets. The results demonstrate clearly that simple network-classification models perform remarkably well-well enough that they should be used regularly as baseline classifiers for studies of relational learning for networked data. The results also show that there are a small number of component combinations that excel, and that different components are preferable in different situations, for example when few versus many labels are known.