GRAPHING NETWORKS
Data Visualization + Information Representation + Python
CONTEXT
Under Filippo Menczer and Clayton Davis' teaching in INFO I-368, I learned how to use python to visualize data in the form of networks. Each student was given a list of manipulations that needed to be performed onto the graph using python. Because this was an individual assignment, there are no other contributors.
OUTCOME
This assignment is largely python programming, however I see the outcome of this assignment as a way to visualize data and represent information. Not only did my capabilities and understanding of python increase immensely, but I also learned that at the most basic level, the colors, shapes, and sizes of the nodes, the direction of the edges, and even the structure of the network convey specific meaning and allows viewers to interpret information.
1.
I used python to make this Erdös-Renyi random graph. The node (circles) sizes are adjusted based the number of degrees (edges) the node has. Since the node size unit is in pixels, for the node sizes to be meaningful, I squared the degree.
