Task 9: Network Assignment Using Golden Record Curation Quiz Data
I used the dataset from our Golden Record Curation Quiz and uploaded the .json file into Palladio to visualize how participants’ musical choices connect to one another. In this network, each node represents either a participant or a piece of music, while each edge shows a shared selection. What I find special is that the resulting visualization forms a web of interconnected tastes, showing clusters of participants linked by similar aesthetic or cultural preferences.
When I examined the Palladio visualization (Figure 1), several noticeable patterns appeared based on the number of edges connecting participants and tracks.
Figure 1
At the center of the network, compositions such as The Well-Tempered Clavier, Brandenburg Concerto (First Movement), and Fifth Symphony (First Movement) formed a dense core cluster. This area connected many participants who shared a preference for classical and orchestral works, reflecting an attraction to structured and historically significant pieces.
Moving outward, a smaller grouping emerged around Bagpipes (Azerbaijan), Tsuru No Sugomori (Crane’s Nest), and Night Chant. This cluster represented participants who selected pieces rooted in folk, Indigenous, or world music traditions, suggesting curiosity toward cultural diversity and traditional soundscapes.
In contrast, more isolated nodes, such as Rite of Spring (Sacrificial Dance) and Johnny B. Goode, extended outward from the main web, highlighting individual or experimental preferences that did not strongly overlap with others. These outliers illustrated how certain musical tastes remain unique within a collective dataset.
Interestingly, a few participants who selected pieces from both classical and world music categories acted as bridges between clusters. Their choices visually linked otherwise separate groups, symbolizing how some individuals naturally cross stylistic and cultural boundaries through music.
In network analysis terms, this visualization maps nodes (participants or tracks), edges (shared choices), and degree centrality, which measures how many connections each node has. As explained in our Module 9 lesson materials, “users like us are, after all, also nodes in the network, and our interests, behaviours, and activities change the relations within the network constantly”.
Each participant’s musical selections increased the weight of certain connections, strengthening ties between particular tracks and individuals. The result was a network organized not by geography or identity but by patterns of shared affinity: a digital echo of how algorithmic systems group people based on their online behaviour and preferences.
While the graph effectively visualizes who is connected to whom through shared musical preferences, it cannot tell us why these choices were made. For example:
- Two participants might both select the same song (e.g. Beethoven’s Fifth Symphony), but one may appreciate its historical influence while another associates it with a personal memory.
- The graph also cannot capture non-selections or the null choices representing tracks we chose not to include. These absences, though invisible in the visualization, hold deep cultural and personal meaning.
This limitation reminds us that what is left out of a dataset, the unchosen, the unheard, is as significant as what appears within it.
The visualization reflects the algorithmic logic underpinning many digital systems today. Like social media recommendation engines, it groups individuals through measurable similarities. Although the image appears neutral, it subtly reinforces assumptions about identity and belonging:
By clustering people based on quantifiable data, it simplifies complex cultural and emotional experiences into neat categories.
It privileges connection over context, what can be measured over what can be felt, mirroring the “attention economy” described in our lesson materials, where algorithms use patterns of behaviour to predict and influence human choices.
Politically, this raises concerns about bias and representation. The visualization lacks metadata such as participants’ backgrounds, emotional motivations, or interpretive reasons. What we see, then, is a partial portrait: a constructed network that omits the richness of human subjectivity, echoing larger debates around data visualization as a tool of both revelation and erasure.
Working on this visualization reminded me how easily technology can flatten personal experiences into abstract data. When I made my selections in the Golden Record quiz, each track carried emotional meaning like memories of travel, family, or teaching moments. Yet, in the Palladio graph, those emotions were reduced to points and lines. This realization made me reflect on my own digital identity: how algorithms categorize me based on patterns rather than stories, and how individuality can resist such systematization. This experience deepened my awareness of the tension between connection and reduction that defines our networked lives.
References
Code.org. (2017, June 13). The internet: How search works [Video]. YouTube. https://youtu.be/LVV_93mBfSU
Cornec, O. (2015). WikiGalaxy: Explore Wikipedia in 3D. Experiments with Google. https://experiments.withgoogle.com/wikigalaxy
Willingham, K. (n.d.). The golden record. Poetry Foundation. https://www.poetryfoundation.org/poems/156957/the-golden-record