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Learning Social Networks from Web Documents Using Support Vector Classifiers

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2 Author(s)
Makrehchi, M. ; Dept. of Electr. & Comput. Eng., Waterloo Univ., Ont. ; Kamel, M.S.

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

Published in:

Web Intelligence, 2006. WI 2006. IEEE/WIC/ACM International Conference on

Date of Conference:

18-22 Dec. 2006