Identifying Influentials in Directed Networks

Research into the identification of influential nodes specifically with regards to weighted, directed networks has been lacking throughout the lifetime of Network Theory as a whole. This research project seeks to propel the field forward through by devising an algorithm aimed at identifying influential nodes through the use of probability propagation models of information transfer through various real-world networks. The networks discussed are a developed test-network using the Price Model of citation network growth, the neuronal connectivity network of the flatworm C. elegans, and Congressional co-sponsorship networks of the USA’s 110th House and Senate. Rankings of influence of each node, as determined by the developed algorithm, were then compared to ground truth rankings of influence determined by Susceptible-Infected-Recovered (SIR) model simulations via the Kendall’s Tau-b statistical measurement, so as to determine the reliability and accuracy of the developed algorithm’s probabilistic approach to influence. To this end, the research finished off by examining how the developed algorithm’s results compare to that of well-known influence calculation mechanisms, such as Google’s PageRank.