Label-dependent Feature Extraction in Social Networks for Node Classification
This addresses the problem of improving classification accuracy for social network analysis, but it appears incremental as it builds on existing feature extraction approaches.
The paper tackles node classification in social networks by proposing a feature extraction method that combines network structure and class labels, resulting in significant accuracy improvements on real-world data.
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.