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Abstract
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Community detection in complex networks is a fundamental task with widespread applications in various domains, including social network analysis, biological systems, recommendation systems, and cybersecurity. Traditional community detection methods often suffer from limitations such as high computational complexity, difficulty in handling high-dimensional and correlated features, and reliance on single-perspective embeddings that fail to capture the full structural and attribute-based information of the graph. These challenges hinder the accuracy and efficiency of existing approaches, especially in large-scale and dynamic networks. This research is essential as it proposes a novel framework that integrates multi-view embedding fusion, preprocessing techniques for feature optimization, and graph-based deep learning for supervised community detection. By addressing the limitations of previous methods, this study aims to improve the accuracy, scalability, and interpretability of community detection models. The increasing demand for efficient and precise network analysis in fields such as fraud detection, disease modeling, and personalized recommendations highlights the importance of developing advanced techniques that can effectively identify meaningful communities in complex structures. Therefore, the proposed research contributes to both theoretical advancements and practical applications, making it a crucial step toward more capable and accurate community detection solutions.
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