We were part of this year’s Girls Talk Math program. This was a great opportunity that we came across in different ways. It has helped us learn about different math topics that we may not have had the opportunity to discover on our own. This topic may seem boring at first, but they all have their individual charms. The topic we chose was Network Science, and together we got to learn about things such as coding on Python.
What is Network Science
Network science is a field of academics in which you study what networks are. You can see what they do and how their connections lead to the way elements can communicate today. Network science uses math and certain codes to display all different types of graphs and sometimes even messages. The most common types of networks found include social networks, computer networks, biological networks, and telecommunication networks. All which are used in everyday life.
The Graph Theory
Graph Theory is a study of visual representations of data, and what the data means for certain topics. Graph theory is used in everyday life, such as helping predict trends in the future, and to help connect people by creating social networks. Along the way we explored Euler paths and circuits, Hamilton paths and circuits, a regular graph, and a complete graph. We studied the various parts of a graph as well, and learned how to graph in python. We learned that nodes are the vertices of the graph (the blue circles in the picture above) and that edges are the lines that connect nodes. Finally, we used some basic python skills to code graphs and solve some problems.
Computing graphs
Making simple graphs in python may seem like a fancy job for professionals, but in actuality it’s not as difficult as it seems. It’s a lot easier when you know what you’re doing. All you have to do is to understand how some basics work (and spell the code correctly) to get the graph you’d like. With some knowledge and facts on coding we were able to create many different graphs.
Centrality and Clustering Coefficient
Centrality in a graph is the importance of a specific node or point. This can be determined by the amount of edges connecting to that node, such that if you removed that node, many other nodes would be affected. Whereas clustering coefficient is a measure of the degree to which nodes in a graph tend to come together. There are many different ways that centrality and clustering coefficients can be represented.
Click here on our work to see code that represents some examples of both
Dolphin code
The dolphin problem was a problem involving a network of dolphins and how they interact. Click Here make sure to scroll until you reach it.
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