The document discusses research into the popular music landscape of the Soviet Union and post-Soviet countries. The research analyzed a dataset of over 4,500 music groups to build a network based on shared musicians and examine the relationship between network centrality measures and the groups' long-term success. Key findings include that the network has strong community structure defined by shared language and musical genres within communities. Centrality measures in the network also correlate with the groups' popularity on Wikipedia, and can somewhat predict success.

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

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Visit Frequency (VF) Correlations

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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

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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

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LINGUOCULTURAL
STRUCTURE

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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