Exploring Signed Social Networks: Algorithms for Community Detection and Structure Analysis

Abstract

The rapid growth of the internet and social networking platforms has significantly enhanced the way individuals connect and share information, often leading to the formation of complex networks with positive and negative relationships. These signed social networks can be represented as undirected graphs with nodes and edges denoting users and their interactions, respectively. Community detection within these networks has become a prominent research area, as it helps to uncover the underlying structure and instability in relationships, thereby predicting organizational changes. This paper reviews the latest advancements in algorithms for community detection in signed networks, focusing on multi-objective optimization approaches that balance modularity and frustration minimization. We explore various methodologies, including ant colony optimization, genetic algorithms, and memetic algorithms, and their applications in identifying community structures. The study highlights the significance of understanding both positive and negative links in social networks to provide a comprehensive analysis of their structural dynamics.