Music culture as a social network (Fall 2019)
MUSIC 466/566: Music Culture as a Social Network
(available for undergraduate or graduate credit) There are no prerequisites for this course. Anyone can take it.
Meetings: Tuesday and Thursday, 11:00 AM - 12:20 PM in Old Arts room #118
- 1 Summary
- 2 Background
- 3 Schedule
- 4 Requirements
- 5 Evaluation
- 6 eClass
- 7 Resources
- 7.1 Books
- 7.2 (Social) Network Analysis Software (all free!)
- 7.3 Research on music networks
- 7.4 Pajek datasets for ESNAP
- 7.5 SNA Datasets, including those relevant to music
- 7.6 Other resources
- 8 Help pages
- 9 Official policy
Ethnomusicology is the study of music in or as society and culture. Social Network Analysis (SNA) is an application of mathematics (graph theory) to social and semantic phenomena. As SNA provides an insightful approach towards understanding society (social links) and culture (semantic links), it follows that SNA also provides an insightful means of thinking in ethnomusicology, and a productive tool for ethnomusicological research. Similarly, graph theory has been applied in many other disciplines - from computer science, to physics, to biology - resulting in a flourishing new field generally known as "network science".
In this course we consider ethnomusicology as "the study of music in, as, or generating networks." With this formulation, networks define the social and cultural contexts within which music operates; the channels through which music, musical artifacts, and knowledge about music, diffuse; and as the basis for musical performance. Music is also considered as a key factor in the formation of social networks. This network approach has become especially pertinent with the rise of social media, through which much music and musical knowledge passes.
Students will learn about social network analysis in theory and practice (including hands-on training using (free) software packages), with applications to music, and will conduct a small research project in this domain.
These days, social networks seem to be everywhere, especially with the advent of "social networking" as a catchphrase; web-based social networking services such as Facebook, Twitter, YouTube, and Instagram; and popularization of social network concepts such as six degrees of separation, and small-world networks. But the idea of using graph theory to understand social groups and culture goes back nearly a century, while social networks themselves are intrinsic to being human. 
The same "network" concept is also used to define "semantic networks" of meaning, using the same analytical techniques that apply to networks of all kinds: locating centers, delineating cohesive subgroups, assessing connectivity, and related operations. In this course we examine both social networks (sonets) and semantic networks (senets), as the basis for doing ethnomusicology.
Ethnomusicology is typically defined as the study of music in society or the study of music as culture...if social network analysis (SNA) is an important approach towards understanding society and culture, then it follows that SNA should also provide an insightful means of thinking in ethnomusicology, and a productive tool for ethnomusicological research: Ethnomusicology is the study of music in social networks. Social networks define music's social and cultural context, as well as the channels through which music, musical knowledge, and knowledge of music makers, diffuse.
Yet few ethnomusicologists have explored SNA's possibilities, perhaps because SNA appears inaccessible, filed under "mathematical sociology," while music scholars have tended to prefer the more qualitative, critical, and interpretive approaches of the human sciences. SNA also presents some challenging methodological difficulties for fieldworkers - mapping social networks is not always easy, practically and ethically. Yet SNA's origins lies in social anthropology, a field with longstanding connections to ethnomusicology. Methodologically SNA is more feasible today, with the emergence of online virtual communities, defined by social networking websites, and other electronic communications. And the basic mathematics required to understand SNA is quite elementary.
This seminar-workshop attempts to bridge the gap between traditional humanistic scholarship and SNA by providing a gentle introduction to methods, theories, and issues in social network analysis,with applications to ethnomusicology. You won’t merely read about social network analysis, you’ll actually do it!
Ethnomusicological applications of SNA include understanding the ways musicians and audiences interact in performance; network aspects of celebrity formation; exploring communities of musical taste; understanding the circulation of online music; analyzing the role of music in shaping social networks, particularly online (including those specifically devoted to music); investigating networks of musical friendship, prestige, and respect; examining linkages between music sites on the Internet; considering networks generated by musical collaborations (e.g. composer-lyricist relations); the overlap of friendship and musical collaboration network; small world networks in the arts (c.f. Degrees of Kevin Bacon); affiliation networks of numerous types; the use of social networks to promote music and musicians, and many other topics.
On a more theoretical level, we'll explore the co-mediation of social networks (sonets) and semantic networks (senets) as providing the basic fabric of social and cultural life.
MCSN 2019 schedule (assignments and class activities, by date).
NOTE: to be updated for 2019
- Classroom work: Lectures (mostly Tuesdays), demos (your demonstrations of Pajek technique), and discussions (more on Thursdays), all interspersed with group exercises, Q/A, videos, demos, etc.
- Homework, promoting a theoretical and practical grasp of social network concepts
- Readings: (theory and methods in ESNAP, 3rd edition (2018), and case studies of applications to music culture)
- Lab: Pajek exercises, described in the ESNAP text (it's very important to do these completely!)
- Problems: to test and reinforce those concepts. Questions are typically due on Thursdays; assignments are typically due the following Tuesday.
- Occasional in-class self-guided group projects (these projects are to be written up and handed in the following class)
- Three very short (20 minute) quizzes, to motivate and assess learning
- Proposal to analyze a public online social network, comprising five short paragraphs and a bibliography (draft to be resubmitted until accepted):
- Identify: Identify a phenomenon from MCSN. Discuss the general aim and value of your study, framed as a contribution to ethnomusicology.
- Contextualize: Provide background (topical and theoretical), citing relevant literature
- Query: List a few research questions inquiring about this phenomenon. What do you want to understand? Provide tentative hypotheses if you have any.
- Model: Theorize your questions using SNA to model the phenomenon. How can you frame the question in terms of social networks? Cite relevant literature, if appropriate.
- Method: What sort of procedure can you propose for gathering data, analyzing it (with Pajek), and answering the questions? Cite relevant literature, if appropriate.
- Bibliography (can be short at this initial stage), including web sites. You may find that INSNA contains useful sources, but your bibliography can include musical as well as SNA materials.
- SNA research project of your choosing, including planning, written proposal, data gathering, preparation of Pajek files, analysis, interpretation, and writeup as a final paper (Music 466: 2500 words; Music 566: 3500 words).
- Class presentation, amply illustrated with Pajek graphs - 10 minutes - outlining your project's main questions and methods, with partial results, during the last few classes at term's end (depending on enrollment).
Note that there is no final exam, but only a final paper. All assignments are to be handed in via eClass, but please bring them to class also (electronically or hard copy) for discussion.
The class meets in a computer lab, but if you'd prefer, bring your laptop to each class, so we can explore the software together and I can help you with software issues.
- This course can be taken at either of two levels: 466 (regular) or 566 (advanced). If you are an undergraduate, you should be enrolled in 466. Graduate students should be enrolled in 566. At the same grade level, expectations for 566 are slightly higher, and the final paper should display greater breadth and sophistication in working with the literature (as well as being 50% longer).
- Word counts do not including bibliography or illustrative graphs and other diagrams.
The evaluation of each requirement is on a scale from 0-4 points. These scores are combined according to the percentages indicated in order to produce a final numeric grade. This grade is rounded to the nearest numeric value in the table below, in order to determine the final letter grade. In exceptional cases the grade A+ may also be assigned. Expectations for the 500 level are higher than for the 400 level. Without a valid excuse grades for late assignments will decremented by a quarter-point per day. Please take care to plan ahead, bearing in mind due dates for your other courses.
All assignments are to be submitted via eClass.
- Attendance and class participation (including Pajek demos): 15%
- Homework (mainly ESNAP "questions" and "assignments", as well as project writeups): 20% total (each submitted item receives an equal weight).
- Quizzes: 6.6666% each (20% total)
- Project proposal: 5%
- Research class presentation: 10%
- Research paper: 30%
eClass will be used to accept assignments. Please do not email assignments or submit hardcopy.
You'll find a number of these books on reserve in the Music Library (Rutherford, 2nd floor), though new rules may preclude placing textbooks there. Some are also available electronically. But I do recommend purchasing the required work (ESNAP), since working from an electronic version may prove awkward as you're also using your computer to work the examples using Pajek.
All print materials on Rutherford Reserve, and several are available free online (you don't have to buy anything!):
- Wouter de Nooy, Andrej Mrvar, and Vladimir Batagelj, Exploratory Social Network Analysis with Pajek (abbreviated: ESNAP), 3rd edition. (Cambridge University Press, 2018). (Available online and in the SUB bookstore.) To be read in conjunction with the free SNA software package Pajek (which was created for PCs but also runs fine on Macs; installation instructions for Mac are here).
- Paul McLean, Culture in Networks
- Guido Caldarelli, Michele Catanzaro, Networks: A Very Short Introduction, OUP Oxford, Oct 25, 2012 Available as an e-book
- Raphaël Nowak, Andrew Whelan, editors, Networked music cultures : contemporary approaches, emerging issues
- Nick Crossley, Siobhan McAndrew, Paul Widdop, eds. Social networks and music worlds.
No need to purchase these at the outset, and several are already available for free online. As for the others, if you find them to your liking you may wish to own a copy. Browse at the library or bookstore.
Texts and reference works
We may use these works to supplement the primary text. I'll make assignments from some of them from time to time; others are listed simply for your reference.
Networks in general
- Mark Newman. Networks (second edition), esp. Chapter 4 on Social Networks and Part II on fundamentals of network theory.
Social Network Analysis
- Robert A. Hanneman and Mark Riddle. Introduction to social network methods (also available as a pdf. Free.) Works well with Netdraw (which runs only on PCs, or Macs in Windows compatibility mode). You may wish to refer to this alternative text to clarify and reinforce understanding, especially if you're having trouble with a concept presented in ESNAP.
- John P Scott, Social Network Analysis: A Handbook, 2nd ed. (Sage Publications Ltd, 2000). (Available in the SUB bookstore.) Provides a succinct summary of the field at a more advanced level.
- Linton C. Freeman, The Development of Social Network Analysis: A Study in the Sociology of Science (Empirical Press, 2004). Outlines the intellectual history of SNA.
- Wasserman, Stanley and Faust, Katherine. Social network analysis: methods and applications. Cambridge, New York: Cambridge University Press; 1994. A rich summary of SNA theory and applications, for those who want a more complete and rigorous reference work.
- Peter J. Carrington. Models and methods in social network analysis. Cambridge, New York: Cambridge University Press; 2005. A collection of more advanced papers on SNA. Available electronically via our library.
- Introduction to Graph Theory, by Robin Wilson (4th edition). A gentle introduction. We may only be reading the beginning of this text.
- Introduction to graph theory [electronic resource / Vitaly I. Voloshin]
Enjoyable and accessible, these books will also stimulate your creative thinking...feel free to browse selectively.
- Albert-Laszlo Barabasi, Linked: How Everything Is Connected to Everything Else and What It Means (Plume, 2003).
- Duncan J. Watts, Six Degrees: The Science of a Connected Age (W. W. Norton & Company, 2004).
- Malcolm Gladwell, The Tipping Point: How Little Things Can Make a Big Difference (Back Bay Books, 2002).
- Nicholas A. Christakis and James H. Fowler, Connected: The Surprising Power of Our Social Networks and How They Shape Our Lives (New York: Little, Brown and Company, 2009). (A popular science treatment.)
(Social) Network Analysis Software (all free!)
Pajek (Slovenian for "spider") is required, as it accompanies our textbook. Pajek runs on Windows, Linux, and Intel Mac platforms. Your first task is to install Pajek on your computer, and begin to get comfortable using it. Help is available. Note that Pajek runs best on PCs, but can also easily be run on a Mac with a bit of prep. I'll help you through it.
Generally, the easiest way to get around the Mac/PC issues for Mac users is to run Windows on your Mac. You can use Bootcamp to do this for free, or invest in a student-discounted version of Parallels.
Optional Network analysis and visualization tools
Optionally, you may like to explore and experiment with other packages...
Orange, for data visualization, analysis, mining...
- Tools at Carnegie Mellon's CASOS
- Graph-tool and NetworkX, free and efficient Python modules for manipulation and statistical analysis of networks.  
- tikz, which produces graphs from latex code
- igraph, an open source C library for the analysis of large-scale complex networks, with interfaces to R, Python and Ruby.
- Orange, a free data mining software suite, module orngNetwork
- Pajek, program for (large) network analysis and visualization.
- Tulip, a free data mining and visualization software dedicated to the analysis and visualization of relational data. 
- SEMOSS, an RDF-based open source context-aware analytics tool written in Java leveraging the SPARQL query language.
- ORA, a tool for Dynamic Network Analysis and network visualization. <ref>Kathleen M. Carley, 2014, ORA: A Toolkit for Dynamic Network Analysis and Visualization, In Reda Alhajj and Jon Rokne (Eds.) Encyclopedia of Social Network Analysis and Mining, Springer.</ref>
- NS-3, a network simulation system
Pajek datasets for ESNAP
Download these sample data sets for use with the textbook, ESNAP. Store them in your Pajek Data directory for future use.
SNA Datasets, including those relevant to music
- Public datasets
- The KONECT Project network list
- Jazz musician network
- Allmusic, containing network data regarding collaborations and styles
- SNA websites
- SNA applications
- SNA demos
- SNA videos
- SNA data
- Netlogo for network simulations
- SNA syllabi
- SNA datasets
Please carefully review the following:
- The University of Alberta is committed to the highest standards of academic integrity and honesty. Students are expected to be familiar with these standards regarding academic honesty and to uphold the policies of the University in this respect. Students are particularly urged to familiarize themselves with the provisions of the Code of Student Behaviour and avoid any behaviour which could potentially result in suspicions of cheating, plagiarism, misrepresentation of facts and/or participation in an offence. Academic dishonesty is a serious offence and can result in suspension or expulsion from the University.”