NETWORKS OF MUSIC GROUPS AS SUCCESS PREDICTORS

Dmitry Zinoviev ()
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Dmitry Zinoviev: Mathematics and Computer Science Department, Suffolk University, 73 Tremont Street, Boston, MA 02421, USA

Advances in Complex Systems (ACS), 2022, vol. 25, issue 05n06, 1-14

Abstract: More than 4600 non-academic music groups had emerged in the USSR and post-Soviet independent nations during 1960–2015, performing in 275 genres. Some groups became legends and survived for decades, while others vanished and are known now only to the most dedicated music history scholars. To explain and predict success, we built a complex network of the groups and their almost 20,000 members based on performers’ sharing using the data from Wikipedia and Google. We calculated the primary network measures: centralities, degree assortativity, and clustering coefficient — and discovered that they could not accurately predict music group success, but they could distinguish between coarse measures of success, such as which groups were above or below the median. In particular, all centralities positively correlate with success, and the clustering coefficient non-linearly maximizes it. The proposed network-based success exploration and prediction methods are transferable to other arts and humanities areas with medium- or long-term team-based collaborations.

Keywords: Music; social network; Russia; USSR (search for similar items in EconPapers)
Date: 2022
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DOI: 10.1142/S0219525922400094

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