When it comes to graph theory, I was trying to find different maps that show trails or streets that I could further describe in detail because that was all I was thinking of to explore while considering the topic of this blog post. Upon further research, I found that graph theory is not limited to maps and roads by any means, it can be found in a variety of examples using things we use in our everyday lives.
I researched multiple different real life applications of graph theory on a list on this website (if you would like to see more real life examples that I don’t discuss you can find it here):
However, the most interesting example I found was regarding social media. The website describes the application of social media to graph theory as: “Connecting with friends on social media, where each user is a vertex, and when users connect they create an edge.” This sounded like a topic that was worth diving a little deeper into, since social media plays such a huge role in our lives.
I paraphrased that sentence and found that this real life application to graph theory is called “Social Network Analysis” or SNA. This is described as being the mapping of relationships between people via different social networking websites, or different inanimate objects like computers, or even URLs. The nodes in the network are defined the people in the groups, while the links between them show a mathematically visual flow of their relationships. When it comes to computers, the nodes would be the computers themselves and the links would be the invisible connection each computer or smart device has when communicating signals back and forth. Below, you will find a visual that shows an example of a Social Networking Analysis:
This network above in particular is known as a “Kite Network”, which was developed by a researcher named David Krackhardt. On the side bar to the right in the image, you can see the Degrees, Betweenness and Closeness information. These are defined as the three different “Centralities” of the image.
The Degree Centrality is the number of direct connections each node (or person) has. As you can see in the image, Diane seems to have the most connections.
The Betweenness Centrality is all about having few direct connections, but having the nodes you are connected with be some of the most popular. In the image, Heather has very few connections, but she is between two very important nodes in the network.
The Closeness Centrality is having very short paths to others, like Fernando and Garth in the image above, but the pattern of their ties allows them to connect with everyone in the network anyways so they can be closer with fewer nodes.
Although this network seems to be neither a circuit nor a path as far as i can tell, (because of the line protruding from the side of the shape, slightly throwing it off) all of the connections are still meaningful because they are between people who created relationships with others and are making more relationships because of the never-ending flow of the social media network.
If you would like to learn more information on this topic, you can visit this website: