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Networks of Music Groups as Success Predictors

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Networks of Music Groups as Success Predictors

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More than 4,600 non-academic music groups emerged in the USSR and post-Soviet independent nations in 1960–2015, performing in 275 genres and sub-genres, including rock, pop, disco, jazz, and folk. Some of the groups became legends and survived for decades, while others vanished and are known now only to select music history scholars and fans. The total number of unique performers in all groups exceeds 17,000, and at least 3,600 of them participated in more than one project.

More than 4,600 non-academic music groups emerged in the USSR and post-Soviet independent nations in 1960–2015, performing in 275 genres and sub-genres, including rock, pop, disco, jazz, and folk. Some of the groups became legends and survived for decades, while others vanished and are known now only to select music history scholars and fans. The total number of unique performers in all groups exceeds 17,000, and at least 3,600 of them participated in more than one project.

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Networks of Music Groups as Success Predictors

  1. 1. Networks of Music Groups as Success Predictors Dmitry Zinoviev Department of Mathematics and Computer Science Suffolk University, Boston
  2. 2. Dmitry Zinoviev * Suffolk University 2 Research Question Who Rocks and Why?
  3. 3. Dmitry Zinoviev * Suffolk University 3 Real Research Questions ● Does sharing performers with other groups influence the groups' eventual success? ● If so, is the success predictable from the performers' sharing network? ● What is the linguocultural and genre structure of the ex-Soviet music universe?
  4. 4. Dmitry Zinoviev * Suffolk University 4 Research Strategy ● Collect data about sharing and success ● Build a network based on shared musicians ● Define “success” ● Correlate network measures (such as centralities) with success measures ● Attempt to predict success from the network measures using machine learning techniques ● Look into genres/languages and communities
  5. 5. Dmitry Zinoviev * Suffolk University 5 DATA
  6. 6. Dmitry Zinoviev * Suffolk University 6 Data Set ● 4,560 non-academic music groups performing in the USSR and post-Soviet countries in 1960–2015 ● 17,000 performers (at least 3,600 shared) ● 275 genres (rock, pop, disco, jazz, folk, etc.) ● Wikipedia pages in 122 languages
  7. 7. Dmitry Zinoviev * Suffolk University 7 New Groups by Year
  8. 8. Dmitry Zinoviev * Suffolk University 8 2,216 Groups on Wikipedia ● Russia ● Estonia ● Ukraine ● Latvia ● Lithuania ● Belarus ● Moldova
  9. 9. Dmitry Zinoviev * Suffolk University 9 NETWORK
  10. 10. Dmitry Zinoviev * Suffolk University 10 Network Construction ● Group → node; labels in the original language ● Two nodes connected if the groups shared at least one musician over their lifetime ● Undirected, unweighted, unconnected graph with no loops and no parallel edges ● For each node, calculate degree, average neighbors degree, closeness, betweenness, and eigenvalue centrality, and clustering coefficient
  11. 11. Dmitry Zinoviev * Suffolk University 11 Network Overview ● Node size represents degree (number of shares)
  12. 12. Dmitry Zinoviev * Suffolk University 12 Network Description ● 80% of the groups (3,602) are in the giant connected component; all other connected components have <13 groups each ● Excellent community structure (m=0.76), 43 communities; each of the largest 25 communities has 20+ groups ● Community = groups that have a lot of mutual musician sharing
  13. 13. Dmitry Zinoviev * Suffolk University 13 SUCCESS
  14. 14. Dmitry Zinoviev * Suffolk University 14 What's “Success”? ● No sales data! ● No charts! ● Informal/semi-legal/illegal status ● Proxies for long-term success (we still remember them!): – Wikipedia page(s) visit frequency within last 3 years (collected from http://stats.grok.se) – Wikipedia page(s) Google PageRank – Available for 2,000 groups
  15. 15. Dmitry Zinoviev * Suffolk University 15 PageRank (PR) Correlations
  16. 16. Dmitry Zinoviev * Suffolk University 16 Visit Frequency (VF) Correlations
  17. 17. Dmitry Zinoviev * Suffolk University 17 Prediction ● Random Decision Forest (RDF) machine learning predictor ● Predict above-median VF vs below-median VF: accuracy 71% (expected by chance: 50%) ● Predict Google PR: accuracy 49% (expected by chance: 17%) ● Quite poor, but not hopeless
  18. 18. Dmitry Zinoviev * Suffolk University 18 GENRES
  19. 19. Dmitry Zinoviev * Suffolk University 19 Genres and Sharing ● Build a network of similar genres (recursive generalized similarity): – Two genres are similar if used by similar groups – Two groups are similar if play similar genres ● Genre → node; two nodes are connected if the genres are “very similar” ● Community structure (m=0.3): – Punk/jazz, metal, disco/pop, blues/hip-hop, light rock
  20. 20. Dmitry Zinoviev * Suffolk University 20 Genre Network Metal Light rock Punk Soul Folk/jazz/hh Disco Ethno Some genres are hierarchical (rock/metal/black metal). TODO: Assign them to different levels.
  21. 21. Dmitry Zinoviev * Suffolk University 21 Musicians Prefer Similar Genres
  22. 22. Dmitry Zinoviev * Suffolk University 22 LINGUOCULTURAL STRUCTURE
  23. 23. Dmitry Zinoviev * Suffolk University 23 Languages, Genres, and Sharing ● Group sharing network has 25 communities with 20+ groups in each ● Preferred language = language of the most frequently visited Wikipedia page ● Look into genres and preferred languages within each community: Are they homo- or heterogeneous?
  24. 24. Dmitry Zinoviev * Suffolk University 24 Genres per Community In 9 communities, >50% of groups perform the one genre. In 23 communities, >50% of groups perform in no more than 2 genres. 71% of all shares— homogeneous
  25. 25. Dmitry Zinoviev * Suffolk University 25 Preferred Languages per Community In 24 communities, >50% of groups have the same preferred language! 84% of all shares —homogeneous
  26. 26. Dmitry Zinoviev * Suffolk University 26 Language and Genre Homogeneity: Either or Both? Language-defined Genre-defined Not very convincing? Mixed
  27. 27. Dmitry Zinoviev * Suffolk University 27 Conclusion ● Musician sharing networks of non-academic music groups in the USSR and post-Soviet countries have community structure inspired by preferred language and musical genre ● Centrality and clustering measures of this network are correlated with long-term success of groups in terms of popularity on Wikipedia and to some extent can serve as success predictors

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