Changes

**'''''This being a clean-up process for the input data before analysis and displayin the form of a graph'''''**Group vertices by cluster '''''(e.g. the Clauset-Newman-Moore algorithm to identify community structures) and calculate clustering coefficient'''''**Count and merge duplicate edges '''''(and therefore scale the resultant edge by width proportional to the number of edges merged)'''''**Layout method - '''''e.g. the Harel-Koren Fast Multiscale Layout algorithm'''''

**Betweenness centrality - '''''identification of corridor/ambassador nodes that are important links between adjacent network communities. In other words, identification of the most BROADLY CONNECTED nodes in the network. Think: few friends in high places, as opposed to an abundance of low-level friends '''''**Closeness centrality - '''''related to clustering coefficient. Identification of strong communities within a larger network'''''**Eigenvector centrality - '''''unclear'''''

**In a nutshell: '''''Highly customizable'''''

**Edge width - '''''can be a function of number of merged edges, etc'''''**Node size/color - '''''can be a function of node's degree, centrality measures, etc'''''**Egonet - '''''user can look at each node as the "center of the network universe"'''''***Pagerank - '''''useful google coefficient that measures how good one node's IN-FLOW is, i.e. the tendency to end up at subject node as agent travels around its neighborhood'''''

***User can view egonets in a matrix, '''''and apply "sort by" such that he can easily identify those nodes with the highest e.g. in/out-degree, centrality, pagerank etc)'''''***Graph density - '''''2*|E|/(|V|*(|V|-1))'''''