Research on music networks

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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 acquaintance networks in relation to musical taste networks.
  • Fan networks - who's a fan of whom?
  • Networks of the social and inanimate: can you construct networks that connect people, groups of people, musical objects, technologies, all interacting?
  • Structure of friendship or collaboration within 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
  • Online networks implicit or explicit - of all types (e.g. social media platforms)
  • Observation of conversational interactions concerning music
  • Observation of performance interactions, especially for improvisational musics
  • Plot networks in films about musicians
  • Ego-alter networks
  • Friendship networks
  • Musical affiliations
  • Economic networks: what is the network of financial flows?
  • Networks of musical production: who is involved and how?
  • 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 do music networks form and change? Who or what shapes them?
  • Who are the opinion leaders or early adopters in music networks and how can they be identified?
  • What is the structure of fame? Who is famous, and how did they get that way? How is fame distributed? Compare across genres or music cultures or historical periods.
  • What are the cohesive communities, as suggested by network data, and how do these correspond to attribute properties of the nodes?
  • What is the impact of technology on the shape of social networks? Can we identify "eras" corresponding to pre-mediated, radio, TV, internet, etc.?
  • How is the homophily hypotheses ("birds of a feather flock together", i.e. that links tend to form among people with similarities) upheld (or not) by music data? Do people who share musical tastes tend to associate? Do the tastes precede such groupings, or follow from them?
  • What is the relationship between friendship and shared musical taste? What factors are most important in establishing friendship?
  • 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?
  • Where are the cohesive sections of a music network, and why are they cohesive? How do they relate to other factors (e.g. gender, age, education, taste, genre...)
  • How do songs spread virally after release? how does musical diffusion vary by genre, country, historical era? How quickly does the "hit" subside?
  • How do collaboration networks vary according to genre? country?
  • Who or what is most important in a network? In what ways, and why is this person so central? (Note: there are many ways to formulate the idea of "centrality", each with a different meaning)
  • 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

Good news! We now have ethics approval to run Google Forms surveys!! see below...

(data collection of various network types, and analysis):

DATA

  • Collecting data by survey instrument with a target population, using Google Forms.
    • Note: you must precede your survey with a preamble following these guidelines, then add a checkbox and blank, allowing the participant to indicate whether they've read the text and agree to participate (check box); if so they "sign" (type their name in the blank).
    • Next you'll have a set of radio buttons or a drop-down list, with one response per individual - the participant selects their name
    • After that, you'll include a series of questions, probably with a set of possible responses as checkboxes, but possibly also using other ways to capture data.
      Some issues to consider when doing this sort of research:
      • Is your group large enough? Too large? Can you be sure you'll collect from everyone? (I suggest around 50 is good, more or less is ok too, but will they respond?)
      • Are you collecting enough data? (it will be hard to get people to go through your survey a second time, so think about all the network and attribute data you might possibly need)
      • Are you collecting too much data? (if the survey is too long people won't finish it!)
      • Are your questions precise enough? (for instance just asking about "friends" might not be - more precisely: how often do you meet?)
      • Is your question too precise? (if so it might be hard to answer, e.g. how many hours per week do you speak with this person.
      • Is your question in good taste as well as ethical? (asking about relationships might be tricky sometimes)
      • Would there be any language barriers?
      • If you're collecting affiliation data (2 mode) do you also have 1 mode data to compare? Some affiliation data isn't all that useful except as attribute data (for instance you *could* create a social network by asking people where they've lived - two people are connected if they've lived in the same city- but how is the resulting network useful?)
      • Have you left enough options so that people can answer without feeling boxed in by the answers you selected?
      • Consider allowing free-format responses (but then you would have to code them, possibly, to come up with networks)
      • Consider a "dry run" with friends to be sure the questions work
  • Snowball sampling (letting each subject refer you to others) online
  • Random sampling of egonetworks (individuals and their alters) online
  • Collecting data online through implicit (e.g. group membership lists, concert attendance lists) or explicit (e.g. Allmusic.com's credits pages, Amazon's "also bought" pages) connections
    • Using "screen scraping" algorithms
    • Using web crawlers (some SNA programs - like SocNetV - contain crawlers; see the list here).
    • Using APIs
    • By hand!
  • Seeking existing datasets with network implications
  • Simulation of real-world music networks, generating simulated data for analysis

ANALYSIS and STATS

  • Affiliation networks & their analysis as one-mode
  • Egonetworks & their analysis (statistical measures are possible with samples)
  • Detecting network clusters and cohesive subnetworks (e.g. k-cores)
  • Detecting node centrality (of different kinds)
  • Measuring network distances and paths for information flow
  • Locating central nodes
  • Often you'll be correlating two partitions: one generated by network data (properties) and the other attribute data (not coming from the network)
  • It's always helpful to be comparative (apples and apples, not apples and oranges!): construct two networks for similar groups and then ask - how do they differ? and why?

Kinds of music networks

  • Affiliation networks (NB: any attribute data can become affiliation network data - you can ask people their level of education and then connect them when they've completed the same level: elementary, JHS, SHS, University.... The question you need to ask yourself is whether such a network is significant in any way? or just attribute data?)
    • 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