Social Network Feature Extraction: Dimensionality Reduction and Classification

Abstract

Social network data contains a wealth of user behavior information, providing a basis for studying user preferences and information dissemination mechanisms in social networks. The high-dimensional and sparse nature of the data poses challenges for social network data analysis. In this paper, we focus on social network feature dimensionality reduction and analysis, and propose a comprehensive framework that integrates dimensionality reduction techniques for social network feature learning. The aim is to extract low-dimensional and efficient feature representations from complex social network data. This framework utilizes the neighboring relationships and similarity measures of nodes to construct features. It employs mainstream dimensionality reduction techniques to reduce the dimensionality of the data, thereby reducing the feature space while preserving critical information. Finally, a classification prediction model is built to accurately predict relationships between unknown nodes. Experimental results on multiple real social network datasets demonstrate that the algorithm proposed in this paper significantly improves the classification performance of social network data.