Big changes! Lots of progress!
As you might have noticed above, I decided to change up the color scheme a bit to help emphasize the data a bit more. Now you can clearly see which nodes have a stronger connection than other nodes. Looking at the GIF above, you will notice a chunk of the nodes connected with a very vivid bright green color while the rest are connected with red/brownish links. These brightly colored links have a stronger connection with one another than the darker colored links. This was true even before the color changes, but they are a bit more emphasized now. Now, in order to attain a bright green color, the link between 2 nodes must be at least 20 or above (changed from the original 3 or above). Because of this shift, you will now notice the presence of a brown color. Because of the way the color scale works, this brown color is represented as roughly the number 10. Meaning that any links colored red are <10, anything brown is ≥10 but <20, and anything green is ≥20. I tried experimenting with different color schemes for fun such as tri-tones and even the color spectrum, but they all seemed too distracting.
Colors are nice and all, but what if we want to know the exact values between the nodes? Well, now it is possible! Before in order to know the value of a link you either had to guess using thickness and color of the line as a reference or open the JSON files are parse the information yourself. Now users can simply hover over a link and see for themselves! Yay!
I noticed that almost all the graphs had pretty low densities compared to what they should have. I quickly realized that the TSP Study nodes were what was causing this inaccurate calculation. After fiddling around with the parser code on Eclipse Java, I managed to get rid of all TSP Study’s, and all Nurses/Doctors involved in the Facebook groups. Every graph shot up significantly in terms of density, and now all the graphs display a more accurate value for their density. Here is a side-by-side comparison of what it used to be vs. what it is now.
I’m currently in the midst of developing a way to automatically assign a node or nodes to be the “central node”, meaning the node with the most connections to other nodes. I have all the algorithms and theories on paper, but I’m contemplating going over all the previous code I have written in the parser and thoroughly cleaning it up. Introducing a node class would be nice, and even the thought of a network class which holds all these nodes is also a possibility. I have a function all written out to calculate a node’s centrality, but I have yet to implement it (something to start working on as soon as I’m done writing this article 🙂 ). This week was very exciting and I look forward to what’s to come next week. Stay beautiful everybody ❤