Difference between revisions of "Music culture as a social network (Fall 2019)"
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Revision as of 08:03, 20 April 2019
short link: http://bit.ly/mcsn19
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.
- 1 Overview
- 2 Schedule
- 3 Requirements
- 4 Evaluation
- 5 eClass
- 6 Resources
- 6.1 Books
- 6.2 (Social) Network Analysis Software (all free!)
- 6.3 Research on music networks
- 6.4 Pajek datasets for ESNAP
- 6.5 SNA Datasets, including those relevant to music
- 6.6 Other resources
- 7 Help pages
- 8 Official policy
These days, social networks seem to be everywhere, especially with the advent of "social networking" as a catchphrase, new web-based social networking services such as Facebook, 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 and semantic networks, often realized as computer networks, 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.
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 the structure of online social networks (Facebook, Twitter, Myspace...and others 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)
- 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: (mostly in ESNAP)
- 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 (Sep 22, Oct 13, Nov 3), to motivate and assess learning
- Proposal to analyze a public online social network, comprising five short paragraphs and a bibliography (draft due Oct 27, 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 the Moodle, but please bring them to class also (electronically or hardcopy) for discussion. The final paper should be submitted electronically, and also in hardcopy.
If possible, bring your laptop to each class, so we can explore the software together.
- 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 (documented medical or family emergency) grades for late assignments will decremented by a quarter-point per day, and make-up quizzes will not be administered. Please take care to plan ahead, bearing in mind due dates for your other courses.
All assignments are to be submitted via the Moodle.
- 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.
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 Pajek (which was created for PCs but also runs fine on Macs).
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; others are listed simply for your reference.
- 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]
- 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.
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.
Optional Network analysis and visualization tools
Optionally, you may like to explore and experiment with other packages...
Orange, for data visualization, analysis, mining...
- 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.”