Week 6: Simplifying Networks

While reading “Demystifying Networks,” I kept wondering how spreadsheets for my final project would fit into the structures that the author was talking about. My data has nodes, but instead of one article of “stuff” per node, I had multiple. For example, in my category of “ingredients,” I have many different foods, and I was afraid that my situation is what the author is describing as difficult to translate into a network. “Once you get to three of more varieties of nodes, most algorithms used in network analysis simply do not work; most algorithms were only created to deal with networks with one variety of node.” If I understood correctly, my spreadsheets contain more than one variety of stuff per node. I scrolled down to the comments, and I found someone with a similar problem to mine – figuring out how to translate my complex data into a network visualization, that is. The author’s response was that although this type of data was possible to visualize through some kind of algorithm, it wasn’t the best way to go about solving this problem. Instead, the author suggested multiple ways of visualizing the data, because one method would not be enough. This is kind of what I was already doing to solve my problem – I had created a word cloud, and the data that the word cloud had narrowed down for me could be something that I plug into a new spreadsheet and visualization. “An option might be to represent each node type individually in a separate representation,” the author suggested, in order to easily translate spreadsheet into something visually readable. Then, these individual representations could be combined to see a bigger picture. Although I wish I could visualize something like the image I attached below of the Flavor Network, the process is too complicated for me to translate into a spreadsheet that visualization programs can read. It also might result in too much data in the visualization, because although the Flavor Network looks interesting and connected and color coordinated, there are a lot of connections that are too small to be interpreted, and therefore that data is lost.

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