Network Analysis of Classical Music Composers' Relationships Based on Knowledge Graph
Keywords:
Hidden Markov Model, Knowledge Graph, Network Analysis, Classical Music, ComposersAbstract
The Hidden Markov Weighted Network Analysis Graph (HMWNag) is a novel and comprehensive framework that combines the power of Hidden Markov Models (HMMs), network analysis, and knowledge graphs to explore the intricate relationships and patterns within the world of music composers and their compositions. In this paper, we present the design and application of the HMWNag, which allows us to uncover hidden creative phases in composers' careers and trace the transitions between these phases. Through incorporating weighted values in the knowledge graph representation, we quantify the strength and significance of relationships between composers and compositions. Applying network analysis techniques to the HMWNag reveals influential composers and communities with shared characteristics, shedding light on musical influences and the evolution of classical music styles over time. Additionally, the HMWNag provides practical applications, including personalized music recommendations and music education programs, enhancing our understanding and appreciation of classical music history. Through this multidimensional approach, the HMWNag emerges as a powerful tool to unravel the complexities of music composers and their works, offering a holistic perspective on the rich tapestry of classical music.
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