Label-dependent Feature Extraction in Social Networks for Node Classification

TL;DR

This paper introduces a novel feature extraction method for social network node classification that combines network structure and node labels, leading to improved accuracy in real-world data.

Contribution

The paper presents a new feature extraction approach that integrates network structure and class labels, enhancing node classification performance.

Findings

Features from the proposed method improve classification accuracy

The influence of different features on performance is analyzed

Experimental results on real-world data validate the method's effectiveness

Abstract

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.