Caltech Stem

     On Thursday, November 14th, I ran into a multitude of issues. All of which was because of pent up anxiety and downright disappointment. It all began in my debate class. I had a tournament coming up soon and my case was barely finished. I know I could finish it late in the day but I felt the need to do it during class. I was compelled to do so since I wanted to do well but I ended up not because I had a math test one period away. For the rest of the class, I did a last-minute cram session but I guess that was bad luck. Right before the test, my teacher, Mrs. Linton, said that if we had 6 minutes left, we "should get on the calculator section ASAP." Judging from her statement, I wrongfully judged that I would need around 10 minutes since I thought she implied that the calc section would take 6. Instead, I really needed much more time. I ended up doing extremely poor and got disappointed. During my "alone" time of thinking about what could I have done to do better

General Overview The Girvan-Newman Algorithm, named after Michelle Girvan and Mark Newman, is a hierarchical method used to detect communities in a graph depending on the iterative elimination of edges with the highest number of shortest paths that go through them. Essentially the algorithm is the process of repetitive calculations of edge betweenness centrality of all nodes and cutting the edge with the highest centrality. In pseudo-code: R epeat until no edges are left: C alculate edge betweenness for every node in the graph R emove the edge with the highest edge betweenness R ecalculate edge betweenness for all remaining nodes C onnected nodes are considered communities This method works because groups are separated from one another, revealing the underlying community structure of the network. Edge betweenness is the driving factor of the algorithm but it must be recalculated after each completed iteration. In sentences, edge betweenness works like the following: for ev