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.

1. Networks of Music Groups as
Success Predictors
Dmitry Zinoviev
Department of Mathematics and Computer Science
Suffolk University, Boston

2. Dmitry Zinoviev * Suffolk University 2
Research Question
Who Rocks and Why?

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. 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. Dmitry Zinoviev * Suffolk University 5
DATA

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. Dmitry Zinoviev * Suffolk University 7
New Groups by Year

8. Dmitry Zinoviev * Suffolk University 8
2,216 Groups on Wikipedia
● Russia
● Estonia
● Ukraine
● Latvia
● Lithuania
● Belarus
● Moldova

9. Dmitry Zinoviev * Suffolk University 9
NETWORK

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. Dmitry Zinoviev * Suffolk University 11
Network
Overview
● Node size
represents
degree
(number of
shares)

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. Dmitry Zinoviev * Suffolk University 13
SUCCESS

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. Dmitry Zinoviev * Suffolk University 15
PageRank (PR) Correlations

16. Dmitry Zinoviev * Suffolk University 16
Visit Frequency (VF) Correlations

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. Dmitry Zinoviev * Suffolk University 18
GENRES

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. 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. Dmitry Zinoviev * Suffolk University 21
Musicians Prefer Similar Genres

22. Dmitry Zinoviev * Suffolk University 22
LINGUOCULTURAL
STRUCTURE

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. 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. 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. Dmitry Zinoviev * Suffolk University 26
Language and Genre Homogeneity: Either or Both?
Language-defined
Genre-defined
Not very convincing?
Mixed

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