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

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

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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?

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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

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Dmitry Zinoviev * Suffolk University 5
DATA

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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

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Dmitry Zinoviev * Suffolk University 7
New Groups by Year

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

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Dmitry Zinoviev * Suffolk University 9
NETWORK

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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

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

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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

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Dmitry Zinoviev * Suffolk University 13
SUCCESS

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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

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Dmitry Zinoviev * Suffolk University 15
PageRank (PR) Correlations

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

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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

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Dmitry Zinoviev * Suffolk University 18
GENRES

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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

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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.

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Musicians Prefer Similar Genres

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Dmitry Zinoviev * Suffolk University 22
LINGUOCULTURAL
STRUCTURE

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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?

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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

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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

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

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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