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Erschienen in:
2010 | OriginalPaper | Buchkapitel
An Assessment of Node Classification Accuracy in Social Networks Using Label-Dependent Feature Extraction
verfasst von : Tomasz Kajdanowicz, Przemysław Kazienko, Piotr Doskocz, Krzysztof Litwin
Erschienen in: Knowledge Management, Information Systems, E-Learning, and Sustainability Research
Verlag: Springer Berlin Heidelberg
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Node classification in Social Network is currently receiving raising attention in the Social Network Analysis research. The main objective of node classification is to assign the correct label to the unlabeled nodes from a set of all possible class labels. This classification task is performed using features extracted from a Social Network dataset. The success of proper feature extraction significantly influences classification accuracy, providing more discriminative description of the data. This paper describes label-dependent features extraction and examines the classification accuracy based on features extracted with this approach. The experiments on real-world data have shown that usage of label-dependent features can lead to significant improvement of classification accuracy.