Learning Social Networks from Web Documents Using Support Vector Classifiers
Masoud Makrehchi, Mohamed S. Kamel · 2006
Automatic generation of a social network requires extracting pair-wise relations of the individuals. In this research, learning social network from incomplete relationship data is proposed. It is assumed that only a small subset of relations between the individuals is known. With this assumption, the social network extraction is translated into a text classification problem. The relations between two individuals are modeled by merging their document vectors and the given relations are used as labels of training data. By this transformation, a text classifier such as SVM is used for learning the unknown relations. We show that there is a link between the intrinsic sparsity of social networks and class distribution imbalance of the training data. In order to re-balance the unbalanced training data, a minority class down-sampling strategy is employed. The proposed framework is applied to a true FOAF (friend of a friend) database and evaluated by the macro-averaged F-measure