Research on music culture as a social network

From Canadian Centre for Ethnomusicology
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Possible project ideas and questions

Generally you (1) locate (or collect) good network data, (2) develop some interesting research questions about that data, which may require collecting more data, and then (3) formulate SNA procedures to answer those questions. Steps 1 and 2 might be reversed.

NETWORK DATA:

  • Structure of friendship or collaboration in a musical group
  • Connecting class members via real or online networks (e.g. shared FB friends)
  • Daily changes in the Twitter egonetwork for music celebrities, correlated to album releases
  • Studies of ethnomusicologists as they collaborate in groups, or affiliate to topics
  • Musical taste implications for social networks
  • Observation of conversational interactions
  • Plot networks in films about musicians
  • Ego-alter networks
  • Friendship networks
  • Musical affiliations
  • Flow: how you learn about music from friends...
  • Literary connections through song lyrics
  • Composer-lyricist networks
  • Social fragmentations induced by media fragmentations
  • Which music networks are scale free?
  • Diameters of music collaboration networks
  • Historical relationship between artists or albums, and newspapers or magazines that review/mention them (many newspapers can be searched online).

SOME QUESTIONS:

  • How does friendship or collaboration in a musical group vary according to personal attributes (gender, age, etc.)? How does it contrast from one group to another?
  • What is the relationship between friendship and shared musical taste? What factors are most important in establishing friendship?
  • How do songs spread virally after release? how does musical diffusion vary by genre, country, historical era?
  • How do collaboration networks vary according to genre? country?
  • Who is most important in a network? How and why? (There are many ways to formulate the idea of "centrality")
  • What is the diameter of different collaboration networks over time, or across genres?
  • How do people learn about new songs and artists? What networks come to bear?
  • How does genre relate to the distribution of fans across artists?
  • What is the structure of "who studied with whom" in various disciplines, including music disciplines. How do they compare, and why the differences?

SOME METHODS:

  • Affiliation network analysis
  • Detecting network clusters
  • Measuring network distances
  • Locating central nodes
  • Other techniques

Kinds of music networks

  • Affiliation networks
    • musicians/groups (musician to group)
    • fan clubs (fan to club)
    • Facebook pages (person to page)
    • musical preference (person to style)
    • concert attendance (person to event)
    • scholars to research areas (topical, theoretical, disciplinary, or geocultural)
  • Statistical implication networks
    • taste implication networks (people who like this music also like this...)
    • Purchase networks (people who bought this also bought this...)
  • friendship networks
    • arbitrary FN loaded with music attributes (taste, performance, consumption, breadth)
    • musician friendship network
  • Legal networks
    • Relationships established by IP ownership
    • Relation of artists to music corporations
    • Relation of music corporations to one another
  • Musical collaboration networks
    • performer collaboration networks
    • composer/lyricist networks
  • Flow networks (diachronic)
    • the diffusion of musical awareness/preference/popularity (how does popularity spread?)
    • the flow of music media: production/distribution/consumption/critical feedback networks
    • genealogical networks
      • transmission - direct teaching and learning networks (formal or informal)
      • musical influence
      • folktune variations
      • musical style and genre development
    • performance interaction
      • musician interactions
      • performer/audience interactions
  • Music theory and composition networks
    • modal, chordal, or pitch networks describing possible musical sequences, as devised by music theorists
    • composition networks (e.g. Lewin and Markov chains), devised by composers, and defining possible musical sequences for human or computer performers (a form of algorithmic composition)
    • networks for free improvisation (e.g. "Electrical Networks")
  • Similarity networks, among
    • musicians
    • songs/pieces
    • styles
  • musical prestige and authority
    • admiration networks (subjective ratings)
    • influence networks (subjective or objective ratings)
  • Musical taste networks
    • musician preference: celebrity topologies
  • aesthetic preference networks (digraph: pairwise judgments on music objects, e.g. "I like A more than B" as "A->B")
  • egonets and music attributes (performance, consumption, taste, breadth)
  • Intertext and hypertext networks
    • citation and co-author networks among music scholars
    • linkages among webpages
    • discursive links via musical terminology
  • Intermusicality networks
    • musical quotation
    • stylistic allusions
  • meta-ethnomusicology
    • Two-mode network of ethnomusicologists and research topics
    • Citation networks and influence
  • web-based networks
    • webpage word co-occurrence (e.g. musical genres)
    • Facebook networks (centered on musicians; or musical friendship links; or embedded music information)
    • Twitter networks (centered on musicians)

Special music network types

Think about the variety of types as applied to music culture:

  • directed vs undirected networks
  • one mode vs two mode networks
  • complete vs subnetworks
  • networks vs. egonets
  • Small world networks (large networks with surprisingly small diameters)
  • time-evolving networks
  • Geographical networks (network formation favors local links )
  • Scale-free networks (network formation favors global hubs)

Network analysis

Two types:

  1. Descriptive and exploratory
  2. Relational:
    1. what is the relation between network properties of nodes, and attribute properties of nodes, in a single network, then seeking explanations for these relations.
    2. Comparative: relating variables across two or more networks and seeking explanations (e.g. why is this network more densely connected than that one?)

Sources of online network data