АRCHITECTURE OF A SCALABLE SYSTEM FOR BEHAVIORAL PATTERN RECOGNITION IN SOCIAL NETWORKS USING GRAPH DATABASES AND FUZZY LOGIC

DOI: 10.31673/2412-4338.2025.038714

Abstract

The subject of this study is the development of modular and intelligent system architecture for behavioral analysis within social networks. With the increasing complexity and volume of user-generated content, there is a growing demand for advanced frameworks capable of understanding, modeling, and predicting user behavior in dynamic and large-scale digital environments. The purpose of this work is to design a service-oriented architecture that integrates graph databases, natural language processing and fuzzy logic to extract semantic insights from social data, identify behavioral patterns, and support real-time visualization and decision-making. The objectives of the study include: formalizing functional and architectural requirements for behavior-aware analytics on heterogeneous data streams; developing a multi-layer fuzzy graph representation of social interactions; designing pattern recognition algorithms that combine fuzzy inference systems and graph-based features to detect trends, anomalies, and community dynamics; and implementing a microservice-based system that ensures scalability, modularity, and interoperability across processing stages. Results obtained: The proposed system constructs semantically enriched fuzzy graphs using multiple fuzzy relations to represent user behavior and interaction intensity. It integrates NLP and fuzzy logic to convert sentiment and thematic signals into interpretable fuzzy annotations stored in the graph structure. The study demonstrates that integrating fuzzy logic and graph analytics within a modular system enables flexible, interpretable, and scalable behavioral analysis in social networks. This approach enhances the ability to detect complex, overlapping behavioral patterns by accounting for both explicit interactions and hidden linguistic signals.

Keywords: information technology, graph databases, fuzzy logic, social networks, computational social science, behavioral analysis, sentiment analysis.