  A new method of feature extraction in the social network for within-network
classification is proposed in the paper. The method provides new features
calculated by combination of both: network structure information and class
labels assigned to nodes. The influence of various features on classification
performance has also been studied. The experiments on real-world data have
shown that features created owing to the proposed method can lead to
significant improvement of classification accuracy.
