The graphs showed the progression of the web between us. At first, each student in this course was an individual node, and we were connected by no means. After our data on track choices were collected, we started to connect to each other by our choices. The connections were undirected, meaning that the connections were bidirectional – if student A is strongly connected to student B, then it is equivalent to say that student B is strongly connected to student A. The connections were also weighted, judging by the different widths that the second graph used for different connecting lines. In the last graph, the choices made by each individual were listed and connected to one another as well. The connections between individuals and song choices were unidirectional because students chose songs but songs could not choose students. The second graph showed the like-mindedness between students in choosing tracks, and the third graph contained even more details of the specific preferences of each student.

Showing the relationship between students and representing the data on track preferences between them is interesting and useful. However, these networks were very quantitative, thereby losing almost all of the qualitative data. For example, every student provided not only their choices but posted their reasonings as well. Although the graphs quantitatively weighted the “like-mindedness” between each student and proportionally represented as the widths of links, they failed to qualitatively evaluate this aspect. Even when two students chose similar tracks, they could be very different in terms of their criteria. If so, their “like-mindedness” is actually low, but this is not the case of the current quantitative web. On the other hand, students with similar mindsets may choose diverse tracks, for example, students who chose tracks that are classic to them, or students who chose tracks that are originated from their homeland. Individuals may have different conceptions of “classic”, and what is classic to one may not be classic to another. Therefore, similar choosing criteria – “choosing classics” – may lead to diverse choices between two like-minded individuals. In the second case, Asian students may prefer Asian music, South American students may prefer their music, and Europeans may prefer European music. Again, similar choosing criteria of “home music” may lead to completely different choices. As a result, students who are not like-minded may be classified as like-minded if based solely on quantitative data (final choices). Graphs, therefore, can be quite handy in representing visual data and the network, on the other hand, can be misleading if qualitative data is ignored.

There is one more concern if the goal of these networks is to establish a community of like-mindedness in golden record music choices. The networks are based on the similarities of track choices, but what about the similarities of the most unwanted choices? For example, two “like-minded” students in this network could be very “unlike-minded” if asking them to choose ten tracks to be eliminated from the golden record. There are many more concerns following this. What if they were asked to choose from a wider range of music? What if they were asked to freely choose a dozen pieces of music that they want to be stored in the golden record? The question list can go on and on. Therefore, graphs can also be misleading in that people may believe in their face value and be convinced by its conclusions without questioning the source of data.