It was really interesting to see everyone’s song choices represented visually in the graph. The first thing I wanted to know was which songs were most popular, so I used Palladio’s node function to identify the larger nodes. I wasn’t surprised that the songs from Australia (Morning Star, Devil Bird) and India (Jaat Kahan Ho) were among the top, but I was surprised that Beethoven’s Fifth Symphony (First Movement) wasn’t more popular, since it’s such a well-known classical piece. I also expected Johnny B. Goode to receive more votes. Seeing other popular songs, like Well-Tempered Clavier and Kinds of Flowers, which I hadn’t chosen, made me want to listen again and understand what others found appealing.
Figure 1. Node size indicating popularity of selected songs.
When I started looking at the groupings, I wondered why I was placed in Group 1. My group seemed to have fewer shared song choices compared to Group 3, which had more overlap. The graph doesn’t show which songs I personally chose or my reasoning (selecting songs to represent different cultures), so that context is invisible. It also made me wonder whether others had similar intentions or were choosing based purely on preference. In this way, the visualization captures patterns but loses individuality and intent, even though these are meaningful parts of the data.
Thinking about this through network theory, as discussed in the Systems Innovations (2015) videos, helped me understand what I was seeing. In Palladio, each person and song acts as a node, and the lines between them are edges showing shared choices. Some nodes are “weighted” because they have more links, like the songs chosen by many people. This reminded me of how the early web evolved into a weighted network, where algorithms like Google’s PageRank began valuing certain connections more highly. Similarly, Palladio’s layout makes popular songs appear central and “important,” while less common ones, like those I chose for diversity, fade into the background. The visualization looks neutral, but it’s guided by hidden rules that privilege certain connections.
I also wondered how the groupings were formed. Was I placed in Group 1 because I chose Johnny B. Goode, a less common pick? If I’d chosen Melanesian Panpipes, which many people selected, would I have been in a different group? I chose Dark Was the Night instead because it spoke to me, yet that choice doesn’t appear in my group. At first, I thought I was reading it wrong, but I realized Palladio only shows songs that connect people within that cluster. The graph also doesn’t show songs we didn’t choose, and these “null” choices can be just as meaningful as the ones we selected. This made me think about how little transparency there is in algorithms; I don’t know how Palladio decided my placement, and even if I did, I’m not sure I could fully understand it.
Figure 2. Palladio visualization showing Group 1 connections.
This experience made me think about the political implications of how data like this is represented. The visualization suggests communities and patterns but leaves out the reasoning and individuality behind each person’s choices. It privileges agreement and visibility over diversity and intent, reflecting how data visualizations can shape power, who gets represented, and whose perspectives fade into the background.
Ultimately, this exercise reminded me that data never tells the whole story. Every visualization reflects choices about what to include and what to ignore. What looks like objective information is really an interpretation, one that shapes how we understand people and culture. This experience helped me see how even simple visualizations can reveal broader questions about how data organizes, simplifies, and sometimes distorts human meaning.
Declaration of AI Assistance
I used ChatGPT (OpenAI, 2025) to assist with refining the clarity and conciseness of my writing; however, all interpretations and ideas presented are entirely my own.
References
OpenAI. (2025, November 2). Used to refine writing [Large language model]. ChatGPT.https://chatgpt.com
Systems Innovation. (2015, April 18). Graph theory overview [Video]. YouTube. https://www.youtube.com/watch?v=9mW9G8jBgmU
Systems Innovation. (2015, April 19). Network connections [Video]. YouTube. https://www.youtube.com/watch?v=JkpX__zLJYI