Module 3 Blog Post
I found the lectures and readings on networks and network visualizations interesting. I especially liked in lecture 11 how Professor Ram described the world as a complex system full of networks. I also enjoyed what Albert Laszlo Barabasi had to say about networks in relation to the human body and how if we understand not only all of the parts of the human body or human genome, but also understand where everything goes (by mapping everything out via network science), then we can repair persons with ailing diseases etc. because we will know know both what part is needed and where it goes. We can diagnose and fix the problem just as a mechanic does. In lecture 11, I liked learning about nodes, edges, directed and undirected relationships, single mode, and two mode networks. It was helpful seeing everyday things like a person’s relationship with another person or a city’s relation to another city to really cement the idea that you really can map out all sorts of things as a network.
Thinking about networks visually allows for more information to be gleaned or processed in a more enriched way. Just as we learned that creating dashboards can be helpful to visually represent information, visualizing a network allows for a more in depth exploration, communication, and understanding (Ram, Lecture 12). It was helpful to see the different types or groupings of visualizations, and how different types/groupings can be helpful depending on the type of data that you are analyzing. Clustering can be helpful when reviewing relationships among a large group of people and geographic layouts can be helpful if studying things like migratory patterns of birds for example.
I found Lecture 13 on Network Properties to be a bit more challenging when it came to calculating a network’s density. I kept getting hung up on n(n-1)/2 where n is the number of vertices but then it finally made sense once I understood that I should first perform the n(n-1)/2 calculation to get the number of possible edges and then make that my denominator. I then use the actual number of edges as my numerator and then perform my calculation of numerator/denominator to get the density. I understand the concept of eigenvector centrality in terms of a node’s eigenvector centrality being based on the eigenvector centrality of other nodes around it. However, I would like to see the formula on how to calculate this. The other topics of Lecture 13 such as the other centrality measures, clustering coefficient, reciprocity, and cliques seemed to be fairly straightforward. I’m excited to use Gephi to create network visualizations in our upcoming Assignment IV!
Thanks for reading,
Josh Edwards
Hi Josh, thanks for sharing, I really enjoyed reading your post. I agree that it is important to recognize that the world is a huge and complex system made up of a bunch of networks. It is so interesting the principles of network analysis can be applied to any network we might encounter, big or small. As you said, the same measurements of networks can be performed on systems within the human body or from one city to another. I also appreciate that you connected back to importance of visually representing networks much like a make visualizations in dashboards. Again, great post, thanks for sharing!
ReplyDeleteHi Josh! Great summary and analysis of this module. I too enjoyed the insights on networks in relation to the human body and how understanding the parts and connections within the body can lead to effective disease treatment. Network visualization was once considered complex, has become an accessible tool for understanding complex relationships within huge data sets. This transformation highlights how technology is not only changing rapidly but also becoming more popular, enabling individuals to explore, communicate, and gain insights from intricate networks in an increasingly intuitive and approachable manner. As we navigate a world where digital networks emphasize much of our daily lives, the ability to quickly grasp, apply, and adapt to new technological tools and concepts becomes increasingly critical.
ReplyDeleteHi Josh, you did a great job highlighting the importance of mapping out networks for diagnosing and solving complex problems, which is an excellent takeaway. I agree, that using every day examples such as social groups or cities and their connection to other social groups or cities really helped grasp on to the whole idea of networks. I think the tools we have available nowadays really helps make data a lot more clear and easier to understand. Overall, great job with your analysis!
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