Multidimensional analysis between high-energy-physics theory citation network and Twitter

The knowledge of information propagation has always been the subject of multiple studies. Recent researches have shown that network with a certain degree of concentration of nodes act often as attractors to others, generating faster and more relevant connections. With this experiment a High-energy-physics theory citation network was explored, in comparison with the influence of a Twitter network. Despite the impact of scientific publication is always not straightforward to capture and measure, a citation network can be represented as a fitting example of a generative process leading to innovation. The investigation has been carried out through network analysis tools, for the purpose to examine common patterns regarding valuable metrics arisen from both multidimensional graphs. Beyond a substantial difference in the usability of the two channels, the results emerged have highlighted important aspects of how the information propagation coefficients are based on similar principles for some metrics, but distant for others, such as the closeness of nodes.