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Soviet Popular Music Landscape
Community Structure and Success
Predictors
Dmitry Zinoviev
Department of Mathematics and Computer Science
Suffolk University, Boston
Dmitry Zinoviev * IC S * Suffolk University  2
Research Question
Who Rocks and Why?
Dmitry Zinoviev * IC S * 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?
Dmitry Zinoviev * IC S * 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
Dmitry Zinoviev * IC S * Suffolk University  5
DATA
Dmitry Zinoviev * IC S * 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 coded genres (rock, pop, disco, jazz, folk, etc.)
● Wikipedia pages in 122 languages
Dmitry Zinoviev * IC S * Suffolk University  7
New Groups by Year
Dmitry Zinoviev * IC S * Suffolk University  8
2,216 Groups on Wikipedia
● Russia
● Estonia
● Ukraine
● Latvia
● Lithuania
● Belarus
● Moldova
Dmitry Zinoviev * IC S * Suffolk University  9
NETWORK
Dmitry Zinoviev * IC S * 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
Dmitry Zinoviev * IC S * Suffolk University  11
Network
Overview
● Node size
represents
degree
(number of
shares)
Dmitry Zinoviev * IC S * 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
Dmitry Zinoviev * IC S * Suffolk University  13
SUCCESS
Dmitry Zinoviev * IC S * 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
Dmitry Zinoviev * IC S * Suffolk University  15
PageRank (PR) Correlations
Dmitry Zinoviev * IC S * Suffolk University  16
Visit Frequency (VF) Correlations
Dmitry Zinoviev * IC S * Suffolk University  17
Prediction (1)
● Random Decision Forest (RDF) machine learning
predictor
● Predict above-median VF vs below-median VF:
accuracy 69% (expected by chance: 50%)
● Predict Google PR: accuracy 50% (expected by
chance: 17%); 95% if 1 error allowed
● Quite poor, but not hopeless
Dmitry Zinoviev * IC S * Suffolk University  18
Prediction (2)
● But isn't visit frequency affected by group size?
(More performers—more search queries?)
● Add group size as a control variable
● Predict above-median VF vs below-median VF:
accuracy 69% (was: 69%)
● No difference!
Dmitry Zinoviev * IC S * Suffolk University  19
GENRES
Dmitry Zinoviev * IC S * Suffolk University  20
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
Dmitry Zinoviev * IC S * Suffolk University  21
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.
Dmitry Zinoviev * IC S * Suffolk University  22
Musicians Prefer Similar Genres
Dmitry Zinoviev * IC S * Suffolk University  23
LINGUOCULTURAL
STRUCTURE
Dmitry Zinoviev * IC S * Suffolk University  24
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?
Dmitry Zinoviev * IC S * Suffolk University  25
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
Dmitry Zinoviev * IC S * Suffolk University  26
Preferred Languages per Community
In 24
communities,
>50% of groups
have the same
preferred
language!
84% of all shares
—homogeneous
Dmitry Zinoviev * IC S * Suffolk University  27
Language and Genre Homogeneity: Either or Both?
Language-defined
Genre-defined
Not very convincing?
Mixed
Dmitry Zinoviev * IC S * Suffolk University  28
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
Dmitry Zinoviev * IC S * Suffolk University  29
Dataset Available
● https://github.com/dzinoviev/sovietmusic
Dmitry Zinoviev * IC S * Suffolk University  30
Made in Pythonia
Get your copy of “Data Science Essentials in Python” at
https://pragprog.com/book/dzpyds/data-science-essentials-in-python

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Soviet Popular Music Landscape: Community Structure and Success Predictors

  • 1. Soviet Popular Music Landscape Community Structure and Success Predictors Dmitry Zinoviev Department of Mathematics and Computer Science Suffolk University, Boston
  • 2. Dmitry Zinoviev * IC S * Suffolk University  2 Research Question Who Rocks and Why?
  • 3. Dmitry Zinoviev * IC S * 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. Dmitry Zinoviev * IC S * 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. Dmitry Zinoviev * IC S * Suffolk University  5 DATA
  • 6. Dmitry Zinoviev * IC S * 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 coded genres (rock, pop, disco, jazz, folk, etc.) ● Wikipedia pages in 122 languages
  • 7. Dmitry Zinoviev * IC S * Suffolk University  7 New Groups by Year
  • 8. Dmitry Zinoviev * IC S * Suffolk University  8 2,216 Groups on Wikipedia ● Russia ● Estonia ● Ukraine ● Latvia ● Lithuania ● Belarus ● Moldova
  • 9. Dmitry Zinoviev * IC S * Suffolk University  9 NETWORK
  • 10. Dmitry Zinoviev * IC S * 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. Dmitry Zinoviev * IC S * Suffolk University  11 Network Overview ● Node size represents degree (number of shares)
  • 12. Dmitry Zinoviev * IC S * 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. Dmitry Zinoviev * IC S * Suffolk University  13 SUCCESS
  • 14. Dmitry Zinoviev * IC S * 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. Dmitry Zinoviev * IC S * Suffolk University  15 PageRank (PR) Correlations
  • 16. Dmitry Zinoviev * IC S * Suffolk University  16 Visit Frequency (VF) Correlations
  • 17. Dmitry Zinoviev * IC S * Suffolk University  17 Prediction (1) ● Random Decision Forest (RDF) machine learning predictor ● Predict above-median VF vs below-median VF: accuracy 69% (expected by chance: 50%) ● Predict Google PR: accuracy 50% (expected by chance: 17%); 95% if 1 error allowed ● Quite poor, but not hopeless
  • 18. Dmitry Zinoviev * IC S * Suffolk University  18 Prediction (2) ● But isn't visit frequency affected by group size? (More performers—more search queries?) ● Add group size as a control variable ● Predict above-median VF vs below-median VF: accuracy 69% (was: 69%) ● No difference!
  • 19. Dmitry Zinoviev * IC S * Suffolk University  19 GENRES
  • 20. Dmitry Zinoviev * IC S * Suffolk University  20 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
  • 21. Dmitry Zinoviev * IC S * Suffolk University  21 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.
  • 22. Dmitry Zinoviev * IC S * Suffolk University  22 Musicians Prefer Similar Genres
  • 23. Dmitry Zinoviev * IC S * Suffolk University  23 LINGUOCULTURAL STRUCTURE
  • 24. Dmitry Zinoviev * IC S * Suffolk University  24 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?
  • 25. Dmitry Zinoviev * IC S * Suffolk University  25 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
  • 26. Dmitry Zinoviev * IC S * Suffolk University  26 Preferred Languages per Community In 24 communities, >50% of groups have the same preferred language! 84% of all shares —homogeneous
  • 27. Dmitry Zinoviev * IC S * Suffolk University  27 Language and Genre Homogeneity: Either or Both? Language-defined Genre-defined Not very convincing? Mixed
  • 28. Dmitry Zinoviev * IC S * Suffolk University  28 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
  • 29. Dmitry Zinoviev * IC S * Suffolk University  29 Dataset Available ● https://github.com/dzinoviev/sovietmusic
  • 30. Dmitry Zinoviev * IC S * Suffolk University  30 Made in Pythonia Get your copy of “Data Science Essentials in Python” at https://pragprog.com/book/dzpyds/data-science-essentials-in-python