One of the areas in which our group does research is analysis of networks based on large-scale Twitter data. Students and postdocs hoping to join this group and do this type of research should familiarize themselves with the methods outlined below:
There is some additional information that may be helpful:
The key here is to carefully read all of the documentation–many of your questions will be answered there.
One breakthrough of our lab is a method to find the most influential nodes in a random network via optimal percolation, with the optimization problem then solved by our Collective Influence (CI) algorithm. While the majority of methods to determine the most influential nodes are trial-and-error-based, ours is based in complex network theory.
Please find here the dataset of Twitter retweets used in the associated paper of Morone and Makse, Influence maximization in complex networks through optimal percolation, Nature 524, 65–68 (2015).