Methods for Identifying Implicit Communities and Analyzing Social Networks Using AI

The article provides an overview of modern methods and algorithms for detecting hidden communities in social networks, a crucial task given the rapid growth of data volumes and the complexity of network structures. The analysis covers clustering approaches and structural characterization of social graphs, including label propagation algorithms, density-based methods, as well as modularity and distributed computing algorithms. The paper explores the identification of implicit connections among users formed by shared interests, social environments, or behavioral patterns. A comprehensive evaluation of approaches to modeling network structures and interpreting interpersonal connections is provided, leveraging text content analysis, social interaction, and influence metrics. Challenges in computational efficiency and algorithm convergence for large-scale data analysis are discussed, alongside proposed improvements for testing and validation methods. Experimental studies validate the developed algorithms on various social graph structures, including real-world and synthetic data, highlighting the influence of topological and behavioral factors on community detection quality. The paper also describes some methods and algorithms for analysing users and communities.