We continued studying graphs with social networking applications in mind. We read “On efficient use of entropy centrality for social network analysis and community detection” by Alexander G. Nikolaev, Raihan Razib, Ashwin Kucheriya which described a measurement of centrality. Think of a social network as network of merchants trading apples with their neighbors. Consider what happens when one of the apple merchants receives an apple from a trade. There are many possible merchants who were the one that sold that apple first. A merchant is considered central to the network if that merchant is highly likely to be the original merchant given for many other apple merchants as measured by the entropy of the corresponding probability distribution.