Abstract
In the era of big data, the quantitative analysis of music influence has become an indispensable dimension. This paper focuses on the quantitative representation of the artist’s influence value and proposes an AR (ArtistRank) value to quantify the influence value, which is obtained during the construction of Influencer-Follower network. Then, Louvain based Music Influencer-follower Network Construction model (LMIFNC) is developed and utilized to construct directed weighted Influencer-Follower network. Experimental results indicate that the AR can quantify the influence value and Influencer-Follower network based on LMIFNC can embody the relationship of different artists.
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Zheng, J., Chi, X., Weng, S. (2021). A Network Based Quantitative Method for the Mining and Visualization of Music Influence. In: Tan, Y., Shi, Y., Zomaya, A., Yan, H., Cai, J. (eds) Data Mining and Big Data. DMBD 2021. Communications in Computer and Information Science, vol 1453. Springer, Singapore. https://doi.org/10.1007/978-981-16-7476-1_6
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DOI: https://doi.org/10.1007/978-981-16-7476-1_6
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