Networks of Music Groups as Success Predictors

Dmitry ZinovievDepartment of Mathematics and Computer ScienceSuffolk University, Boston

Research Question

Who Rocks and Why?

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?

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

DATA

Data Set

4,560 non-academic music groups performing in the USSR and post-Soviet countries in 19602015

17,000 performers (at least 3,600 shared)

275 genres (rock, pop, disco, jazz, folk, etc.)

Wikipedia pages in 122 languages

New Groups by Year

2,216 Groups on Wikipedia

Russia

Estonia

Ukraine

Latvia

Lithuania

Belarus

Moldova

NETWORK

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

Network Overview

Node size represents degree (number of shares)

Network Description

80% of the groups (3,602) are in the giant connected component; all other connected components have