In this project that made with Palladio, I created a network with two types of nodes: classmates and tracks (songs). Each line, or edge, shows that a classmate chose a song. Every line means one person picked that track. The size of each track node shows how many classmates selected that song. A larger node means more people liked it. All classmate nodes are the same size. In the top right corner, there is a small example. Two person are both connected to Track 7. This means they both chose the same song. In the top left corner, one person connects to only one song, Track 16. She was the only person who picked it.

In the middle of the image, there are many track nodes with different sizes. Bigger ones show songs that were chosen more often. This helps us see which songs were popular and which classmates share similar choices. It can be seen around, people as nodes connecting to the tracks placed in the center of the page. It seems they linked to one another by the tracks. I did not give special weights to classmates. I wanted the focus to stay on how songs connect people through shared preferences. If I wanted to explore more, I could use network tools to see which songs or classmates are most connected. This could reveal small groups that enjoy similar music.

When I looked at the weighted tracks, I noticed some surprising results. For example, track 13 (a song from Peru) and track 17 (The Well-Tempered Clavier- Bach) were chosen by many classmates. They were selected more often than track 18 (Beethoven’s Symphony No. 5) or track 11 (The Magic Flute-Mozart). It was also interesting that track 2 (a song from Java) had one of the highest numbers of selections overall.

This shows something important. We all respect diversity, but our choices were very different from those made by NASA for its Golden Record fifty years ago. NASA’s selections reflected an Anglo-American and Euro-classical bias. Two pieces were by Beethoven, and most were from Western composers. This reflects what Williamson (2019) calls “expert networks shaping cultural and technological narratives” in education and media.

Today, after fifty years, the world has changed. Views about culture and diversity are much broader. The type of selection NASA made no longer feels fully fair or representative. What inspired me in my classmates’ network was that their choices naturally showed respect for diversity. Without aiming for perfect equality, they reached a kind of geographical and aesthetic balance. For example, the song from Peru received far more attention than expected. This revealed a wider and fairer idea of what beauty in music can mean. I believe this is a more truthful picture of our planet. If we ever send a new version of the Golden Record into space, it should truly represent all world cultures, not just a limited group of Western traditions.

This project also connects to what graph-based text models. Each connection in a network carry meaning beyond words. It shows hidden structures of interpretation (Sánchez-Antonio et al., 2024). In my case, every edge connected not only a listener to a track but also one worldview to another. Together, these edges formed a cultural web like semantic graphs in natural-language processing. As Nabhan and Shaalan (2016) explain, “graph structures reveal larger patterns beyond simple text analysis.” My visualization showed similar results. It revealed how people connect through shared art and emotion.

So far, we have explored many ways to express and document information through images, emojis, hyperlinks, and voice recordings. Adding network visualization takes this further. We are not just documenting data; we are showing relationships. Gonçalves et al. (2022) noted that “graph representation refines understanding of complex data and reveals missing links between entities.” Networking lets us see connections between cultures more clearly. Each node can lead to another person, idea, or song. This shows how knowledge and culture intertwine in meaningful ways. This visualization also shows that all world cultures share a common origin in humanity. Networking helps us see that link clearly. Each line (edge) can lead to a new node, and each node connects us to another person, idea, or culture. In more complex networks, this method helps us reach multiple resources at the same time. We can explore and compare diverse information more efficiently. This allows technology to bring knowledge and cultures closer together.

When we use emojis, written text, or voice recordings, our communication usually happens in one dimension. It moves from one idea to the next. With hyperlinks, our view becomes two-dimensional. Clicking a link takes us to another page and opens a new layer of meaning. This made sharing and accessing information faster and easier. Networking, however, takes this even further. It creates a kind of three-dimensional world. In a network, we can see many connections at once. We do not move one step at a time. Instead, we can explore several links together, choose which ones to follow, and make thoughtful choices about where to go next. In a traditional hyperlink system, we must leave one page to reach another. But in a network visualization, we can see the whole structure at the same time. We can decide which paths matter and explore them freely.

The example we used here is simple, but the idea becomes much more powerful in complex social or cultural systems. Networking allows information to flow faster. It also helps us understand the relationships between ideas, systems, and cultures more clearly. This makes learning and knowledge-sharing more connected, inclusive, and dynamic.

Author’s Note: Because of my limited experience with Palladio, I could not use all its features in this project. I believe the software offers more tools and options than I explored. From my experience with other network visualization programs (Network Meta-analysis in health systems), I know that both node size and edge thickness can change. These settings help show different kinds of relationships. For example, the thickness of an edge can represents how much similarity or shared data exists between two nodes or two data points. In this project, the edge weight did not appear meaningful in my visualization. This was probably because I am still learning how to use Palladio

References

Gonçalves, L. B., Nesic, I., Obradovic, M., Stieltjes, B., Weikert, T., & Bremerich, J. (2022). Natural language processing and graph theory: Making sense of imaging records in a novel representation frame. JMIR Medical Informatics, 10(12), e40534. https://doi.org/10.2196/40534

Nabhan, A. R., & Shaalan, K. (2016). A graph-based approach to text genre analysis. Computación y Sistemas, 20(3), 527–539. https://doi.org/10.13053/CyS-20-3-2471

Sánchez-Antonio, C., Valdez-Rodríguez, J. E., & Calvo, H. (2024). TTG-Text: A graph-based text representation framework enhanced by typical testors for improved classification. Mathematics, 12(22), 3576. https://doi.org/10.3390/math12223576

Williamson, B. (2019). New power networks in educational technology. Learning, Media and Technology, 44(4), 395–398. https://doi.org/10.1080/17439884.2019.1672724