Relationship Between PageRank and Graph Theory
https://www.wired.com/insights/2014/03/graph-theory-key-understanding-big-data/
Google has, invariably, become a necessity in our lives. From asking simple questions about the world to searching for an article to write for a Networks blog post, using Google is practically second nature at this point. Using PageRank algorithms, Google is able to come up with the most relevant search topics to really maximize efficiency for users. These algorithms are able to make connections between related topics, and they allow users to find the most relevant information quicker. From Google’s end, they are able to represent this data in a graph. Since many users do not enter the most descriptive queries, Google can make graphs of relationships between web pages in order to identify the best information. A graph allows for visualization of all of the relevant queries that normal algorithms alone couldn’t; this allows for the PageRank techniques to be enhanced and for users to be satisfied with the information they find. In the future, graphs will likely be applied anywhere; constructing graphs allow for data to be analyzed in such a way that answers to unknowns will be intuitive.
This applies to class because it connects the ideas of PageRank and Graph Theory. First, the article talks about Google’s PageRank algorithm and how it judges the value of a webpage. It talks about authoritative information sources and how pages are linked. As in lecture, Google can keep performing updates to represent quantitatively the value of a webpage and its links. These hub and authoritative updates keep search results relevant and optimize to what is most appropriate for a user. These results, additionally, can apply to graph theory. With webpages as nodes and hyperlinks as directed edges, Google is able to visually see how the webpages are linked. If there are multiple edges to one webpage, then perhaps that webpage is more relevant and as such, should appear higher up in a search result. Additionally, the author postulates that graph theory can be applied to help solve any big data problem. Graph theory allows for data to be looked at in such a manner that interpreting it is easy.