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Social Network Analysis of Major U.S. Orchestras and its Implications for Collegiate Musicians

Presented By: Andrew White, Brian Dinh, Jeffrey Ryan, Jonathan Zhang, Samarth Arul

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Motivation

  • High education costs and employment anxiety for young musicians
  • Understanding pathways from conservatories to orchestras
  • Data could highlight where career pipelines are strong–helping musicians and schools realize allocate resources

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Questions Analyzed

  • Question #1:
    • What basic network structures are prevalent in the data?
  • Question #2:
    • Centrality: What can centrality reveal about the behavior of our network generally?
  • Question #3:
    • Prestige: What does the prestige of a school tell us about its place in the network?

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Data Collection Methodology

  • Data from top 5 U.S. orchestras: NYC, Philadelphia, Boston, LA, Chicago
  • Collected information on performers (Python/Beautiful Soup + OpenAI GPT API)

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Data Collection (Cont’d)

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Figure 1: Plot of musician-orchestra-school network

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Question 1: Network Structures (ERGM)

  • Objective: Examine global properties of the musician-school-orchestra network using ERGM (Exponential Random Graph Model).
  • Findings: (1) Edges: 305 times less likely to exist in our network compared to random networks. (2) Prestigious nodes are 14% more likely to be involved in connections. (3) Absence of triangles; network composed entirely of chains.

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Question 2: Centrality

  • Objective: Analyze the network through centrality metrics to understand node roles.
  • Findings:
    • Top performers similar across most centrality metrics.
    • Authority centrality rankings differ significantly.
  • Hypothesis: Differences in authority centrality due to unique roles in knowledge transfer.

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Question 2: Centrality

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Question 3: Prestige

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Question 3: Prestige (Cont’d)

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Recommendations to Client

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  • For Students: Seek a prestigious school OR a good instructor
  • For Institutions: Strengthen ties with top orchestras and notable professors
  • Geographic Influence: Proximity to prestigious schools can impact career prospects
  • Notable: Musicians are often in positions as brokers from the orchestras to the schools where they studied.

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Actionable Implications, Areas for Future Research

  • Expand analysis to more orchestras and additional dimensions (competitions, chamber groups)
  • Conduct time series analysis for pipeline changes
  • Infer connections for a denser, more nuanced network analysis

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Thank you.