Production of social network data in different kinds and huge amounts brings with it classification problems which need to be solved. In this chapter, we introduce a framework for classification in a social network. Aggregation of neighbor labels and sampling are two important aspects of classification in a social network. We give details of different aggregation methods and sampling methods. Then, we discuss different graph properties, especially homophily, which may be helpful in determining which type of classification algorithm should be used. We give details of a collective classification algorithm, ICA (Iterative Classification Algorithm), which can be used for semi-supervised learning in general and transductive learning in particular on a social network. We present classification results on three different datasets, using different aggregation and sampling methods and classifiers.