Information flow dynamics and timing patterns in the arrival of email viruses

Analysis of the timing of the arrival of email viruses at different computers provides a way of probing the structural and dynamical properties of the Internet. We found that the intervals t between the arrival of four different strains of email viruses have a power law distribution proportional to $t^{-d},$ where $1.5<~d<~3.2$ and that there are positive correlations between these intervals. Salient features of the data were reproduced with a model having subnetwork units of different size where the structural components and the dynamical components all have power law scaling relationships with the size of the units. This is an assumption, that we hope will encourage empirical evaluation of these relationships.